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Advances, Applications, and Challenges

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Part of the book series: Advances in Information Security ((ADIS,volume 59))

Abstract

Biometric cryptosystems and cancelable biometrics offer several advantages over generic biometric systems. The most important advantages are summarized in Table 15.1. These major advantages over conventional biometric systems call for several applications. In order to underline the potential of both technologies two essential use cases are discussed in detail. With respect to the design goals, biometric cryptosystems and cancelable biometrics offer significant advantages to enhance the privacy and security of biometric systems, providing reliable biometric authentication at a high security level. Techniques which provide provable security/privacy, while achieving practical recognition rates have remained elusive (even on small datasets).

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References

  1. Aarabi, P., Lam, J., Keshavarz, A.: Face detection using information fusion. In: Proceedings of 10th International Conference on Information Fusion, pp. 1–8. IEEE, New York (2007). doi: 10.1109/ICIF.2007.4408078

    Google Scholar 

  2. Abhyankar, A., Schuckers, S.: Active shape models for effective iris segmentation. In: Flynn, P.J., Pankanti, S. (eds.) Biometric Technology for Human Identification III, Proceedings of SPIE, vol. 6202, pp. 62,020H.1–10. SPIE, Bellingham, WA (2006). doi: 10.1117/12.666435

    Google Scholar 

  3. Abhyankar, A., Schuckers, S.: Iris quality assessment and bi-orthogonal wavelet based encoding for recognition. Pattern Recogn. 42, 1878–1894 (2009). doi: 10.1016/ j.patcog.2009.01.004

    MATH  Google Scholar 

  4. Adler, A.: Sample images can be independently restored from face recognition templates. In: Proceedings of Canadian Conference on Electrical and Computer Engineering, vol. 2, pp. 1163–1166. IEEE, New York (2003). doi: 10.1109/CCECE.2003.1226104

    Google Scholar 

  5. Adler, A.: Vulnerabilities in Biometric Encryption Systems. In: Kanade, T., Jain, A., Ratha, N. (eds.) Proceedings of 5th International Conference on Audio- and Video-Based Biometric Person Authentication. LNCS, vol. 3546, pp. 211–228. Springer, New York (2005). doi: 10.1007/11527923_114

    Google Scholar 

  6. Ahmad, S., Lu, Z.M.: A joint biometrics and watermarking based framework for fingerprinting, copyright protection, proof of ownership, and security applications. In: Proceedings of International Conference on Computational Intelligence and Security Workshops, pp. 676–679. IEEE, New York (2007). doi: 10.1109/CISW.2007.4425586

    Google Scholar 

  7. Ahmed, F., Moskowitz, I.: Composite signature based watermarking for fingerprint authentication. In: Proceedings of 7th Workshop on Multimedia and Security, pp. 799–802. ACM, New York (2005). doi: 10.1145/1073170.1073195

    Google Scholar 

  8. Al-Raisi, A., Al-Khouri, A.: Iris recognition and the challenge of homeland and border control security in UAE. Telematics Inform. 25(2), 117–132 (2008). doi: 10.1016/j.tele.2006.06.005

    Google Scholar 

  9. Alclear: Clear. URL http://clearme.com. Retrieved May 2012

  10. Alles, E., Geradts, Z., Veenman, C.: Source camera identification for heavily jpeg compressed low resolution still images. J. Forensic Sci. 54(3), 628–638 (2009). doi: 10.1111/j.1556-4029.2009.01029.x

    Google Scholar 

  11. Almeida, P.: A knowledge-based approach to the iris segmentation problem. Image Vis. Comput. 28(2), 238–245 (2010). doi: 10.1016/j.imavis.2009.07.003

    MathSciNet  Google Scholar 

  12. Alonso-Fernandez, F., Tome-Gonzalez, P., Ruiz-Albacete, V., Ortega-Garcia, J.: Iris recognition based on sift features. In: Proceedings of International Conf on Biometrics, Identity and Security, pp. 1–8. IEEE, New York (2009). doi: 10.1109/BIDS.2009.5507529

    Google Scholar 

  13. Ang, R., Safavi-Naini, R., McAven, L.: Cancelable key-based fingerprint templates. In: Boyd, C., Nieto, J.G. (eds.) Proceedings of 10th Australasian Conference on Information Security and Privacy. LNCS, vol. 3574, pp. 242–252. Springer, New York (2005). doi: 10.1007/11506157_21

    Google Scholar 

  14. ANSI: Safe Use of Lasers and LEDs Used in Optical Fiber Transmission Systems, ANSI Z136.2 (1988)

    Google Scholar 

  15. Ao, M., Li, S.: Near infrared face based biometric key binding. In: Tistarelli, M., Nixon, M. (eds.) Proceedings of 3rd International Conference on Biometrics. LNCS, vol. 5558, pp. 376–385. Springer, New York (2009). doi: 10.1007/978-3-642-01793-3_39

    Google Scholar 

  16. Arakala, A.: Secure and private fingerprint-based authentication. Bull. Aust. Math. Soc. 80(2), 347–349 (2009). doi: 10.1017/S0004972709000665

    MathSciNet  Google Scholar 

  17. Arul, P., Shanmugam, A.: Generate a key for AES using biometric for VOIP network security. J. Theor. Appl. Inform. Tech. 5(2), 107–112 (2009)

    Google Scholar 

  18. Arvacheh, E., Tizhoosh, H.: Iris segmentation: Detecting pupil, limbus and eyelids. In: Proceedings of IEEE International Conference on Image Processing, pp. 2453–2456. IEEE, New York (2006). doi: 10.1109/ICIP.2006.312773

    Google Scholar 

  19. Authenti-Corp: Iris Recognition Study 2006 (IRIS06) (2007). URL http://www.authenti-corp.com/iris06/report/IRIS06\_final\_report\_v1-0\_20070901.pdf. Retrieved May 2012

  20. Ballard, L., Kamara, S., Monrose, F., Reiter, M.: On the requirements of biometric key generators. Tr-jhu-spar-bkmr-090707, JHU Department of Computer Science (2007)

    Google Scholar 

  21. Ballard, L., Kamara, S., Monrose, F., Reiter, M.K.: Towards practical biometric key generation with randomized biometric templates. In: Proceedings of 15th ACM Conference on Computer and Communications Security, pp. 235–244. ACM, New York (2008). doi: 10.1145/1455770.1455801

    Google Scholar 

  22. Ballard, L., Kamara, S., Reiter, M.: The practical subtleties of biometric key generation. In: Proceedings of 17th Conference on Security Symposium, pp. 61–74. USENIX (2008)

    Google Scholar 

  23. Barni, M., Bartolini, F., Piva, A.: Improved wavelet-based watermarking through pixel-wise masking. IEEE Trans. Image Process. 10(5), 783–791 (2001). doi: 10.1109/83.918570

    MATH  Google Scholar 

  24. Bartlow, N., Kalka, N., Cukic, B., Ross, A.: Protecting iris images through asymmetric digital watermarking. In: IEEE Workshop on Automatic Identification Advanced Technologies, vol. 4432, pp. 192–197. IEEE, New York (2007). doi: 10.1109/AUTOID.2007.380618

    Google Scholar 

  25. Basit, A., Javed, M.: Iris localization via intensity gradient and recognition through bit planes. In: Proceedings of IEEE International Conference on Machine Vision, pp. 23–28. IEEE, New York (2007). doi: 10.1109/ICMV.2007.4469267

    Google Scholar 

  26. Belaroussi, R., Milgram, M., Prevost, L.: Fusion of multiple detectors for face and eyes localization. In: Proceedings of International Symposium Image and Signal Processing and Analysis, pp. 24–29. IEEE, New York (2005). doi: 10.1109/ISPA.2005.195378

    Google Scholar 

  27. Belaroussi, R., Prevost, L., Milgram, M.: Multi-stage fusion for face localization. In: Proceedings of International Conference on Information Fusion, pp. 1–8. IEEE, New York (2005). doi: 10.1109/ICIF.2005.1591996

    Google Scholar 

  28. Belcher, C., Du, Y.: A selective feature information approach for iris image-quality measure. IEEE Trans. Inform. Forensics Secur. 3(3), 572–577 (2008). doi: 10.1109/TIFS.2008.924606

    Google Scholar 

  29. Belcher, C., Du, Y.: Region-based sift approach to iris recognition. Optic Laser Eng. 47(1), 139–147 (2009). doi: 10.1016/j.optlaseng.2008.07.004

    Google Scholar 

  30. Belhumeur, P., Jacobs, D., Kriegman, D., Kumar, N.: Localizing parts of faces using a consensus of exemplars. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 545–552. IEEE, New York (2011). doi: 10.1109/CVPR.2011.5995602

    Google Scholar 

  31. Beng, A., Teoh, J., Toh, K.A.: Secure biometric-key generation with biometric helper. In: Proceedings of 3rd IEEE Conference on Industrial Electronics and Applications, pp. 2145–2150. IEEE, New York (2008). doi: 10.1109/ICIEA.2008.4582898

    Google Scholar 

  32. Berggren, L.: Iridology: A critical review. Acta Ophthalmol. 63(1), 1–8 (1985). doi: 10.1111/j.1755-3768.1985.tb05205.x

    MathSciNet  Google Scholar 

  33. Bertillon, A.: La couleur de l’iris. In: Annales de demographie internationale, pp. 226–246 (1886)

    Google Scholar 

  34. Bishop, C.: Pattern Recognition and Machine Learning (Information Science and Statistics), 1st edn. 2006. corr. 2nd printing edn. Springer, New York (2007)

    Google Scholar 

  35. Blythe, P., Fridrich, J.: Secure digital camera. In: Proceedings of Digital Forensic Research Workshop, pp. 1–12. Ithaca, NY (2004)

    Google Scholar 

  36. Bodo, A.: Method for producing a digital signature with aid of a biometric feature (1994). German patent DE 42 43 908 A1

    Google Scholar 

  37. Boles, W., Boashash, B.: A human identification technique using images of the iris and wavelet transform. IEEE Trans. Signal Process. 46(4), 1185–1188 (1998). doi: 10.1109/78.668573

    Google Scholar 

  38. Bolle, R., Connell, J., Pankanti, S., Ratha, N., Senior, A.: Guide to Biometrics. Springer, New York (2004)

    Google Scholar 

  39. Boult, T.: Robust distance measures for face-recognition supporting revocable biometric tokens. In: Proceedings of 7th International Conference on Automatic Face and Gesture Recognition, pp. 560–566. IEEE, New York (2006). doi: 10.1109/FGR.2006.94

    Google Scholar 

  40. Boult, T., Scheirer, W.: Bio-cryptographic protocols with bipartite biotokens. In: Proceedings of IEEE Biometric Symposium, pp. 9–16. IEEE, New York (2008). doi: 10.1109/BSYM.2008.4655516

    Google Scholar 

  41. Boult, T., Scheirer, W.: Bipartite biotokens: Definition, implementation, and analysis. In: Tistarelli, M., Nixon, M. (eds.) Proceedings of 3rd International Conference on Biometrics. LNCS, vol. 5558, pp. 775–785. Springer, New York (2009). doi: 10.1007/978-3-642-01793-3_79

    Google Scholar 

  42. Boult, T., Scheirer, W., Woodworth, R.: Revocable fingerprint biotokens: Accuracy and security analysis. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE, New York (2007). doi: 10.1109/CVPR.2007.383110

    Google Scholar 

  43. Bowyer, K.: The results of the nice.ii iris biometrics competition. Pattern Recogn. Lett. 33(8), 965–969 (2012). doi: 10.1016/j.patrec.2011.11.024

    Google Scholar 

  44. Bowyer, K., Hollingsworth, K., Flynn, P.: Image understanding for iris biometrics: A survey. Comput. Vis. Image Understand. 110(2), 281–307 (2007). doi: 10.1016/j.cviu.2007.08.005

    Google Scholar 

  45. Bowyer, K., Hollingsworth, K., Flynn, P.: A survey of iris biometrics research: 2008–2010. In: Handbook of Iris Recognition. Springer, New York (2012)

    Google Scholar 

  46. Boyce, C., Ross, A., Monaco, M., Hornak, L., Li, X.: Multispectral iris analysis: A preliminary study. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshop, pp. 51–59. IEEE, New York (2006). doi: 10.1109/CVPRW.2006.141

    Google Scholar 

  47. Boyen, X.: Reusable cryptographic fuzzy extractors. In: Proceedings of 11th ACM Conference on Computer and Communications Security, pp. 82–91. ACM, New York (2004). doi: 10.1145/1030083.1030096

    Google Scholar 

  48. Braun, D.: How they found National Geographic’s “Afgahn Girl” (2003). URL http://news.nationalgeographic.co.uk/news/2002/03/0311\_020312\_sharbat.html. Retrieved May 2012

  49. Breebaart, J., Busch, C., Grave, J., Kindt, E.: A reference architecture for biometric template protection based on pseudo identities. In: Proceedings of Biometrics and Electronic Signatures, pp. 25–38. Ges. Informatik (2008)

    Google Scholar 

  50. Bresenham, J.: Algorithm for computer control of a digital plotter. IBM Syst. J. 4(1), 25–30 (1965). doi: 10.1145/280811.280913

    Google Scholar 

  51. Bringer, J., Chabanne, H., Cohen, G., Kindarji, B., Zemor, G.: Optimal iris fuzzy sketches. In: Proceedings of IEEE 1st International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–6. IEEE, New York (2007). doi: 10.1109/BTAS.2007.4401904

    Google Scholar 

  52. Bringer, J., Chabanne, H., Cohen, G., Kindarji, B., Zemor, G.: Theoretical and practical boundaries of binary secure sketches. IEEE Trans. Inform. Forensics Secur. 3, 673–683 (2008). doi: 10.1109/TIFS.2008.2002937

    Google Scholar 

  53. Bringer, J., Chabanne, H., Kindarji, B.: The best of both worlds: Applying secure sketches to cancelable biometrics. Sci. Comput. Program. 74(1-2), 43–51 (2008). doi: 10.1016/j.scico.2008.09.016

    MathSciNet  MATH  Google Scholar 

  54. Bringer, J., Chabanne, H., Kindarji, B.: Anonymous identification with cancelable biometrics. In: Proceedings of 6th International Symposium on Image and Signal Processing and Analysis, pp. 494–499. IEEE, New York (2009)

    Google Scholar 

  55. Bringer, J., Despiegel, V.: Binary feature vector fingerprint representation from minutiae vicinities. In: Proceedings of IEEE 4th International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–6. IEEE, New York (2010). doi: 10.1109/BTAS.2010.5634488

    Google Scholar 

  56. Broussard, R., Ives, R.: Using artificial neural networks and feature saliency to identify iris measurements that contain the most discriminatory information for iris segmentation. In: Proceedings of IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications, pp. 46–51. IEEE, New York (2009). doi: 10.1109/CIB.2009.4925685

    Google Scholar 

  57. Broussard, R., Kennell, L., Soldan, D., Ives, R.: Using artificial neural networks and feature saliency techniques for improved iris segmentation. In: Proceedings of International Joint Conference on Neural Networks, pp. 1283–1288. IEEE, New York (2007). doi: 10.1109/IJCNN.2007.4371143

    Google Scholar 

  58. Bruyndonckx, O., Quisquater, J.J., Benoit, M.: Spatial method for copyright labeling of digital images. In: Pitas, I. (ed.) Proceedings of IEEE International Workshop on Nonlinear Signal and Image Processing, pp. 456–459. NAT, Thessaloniki (1995)

    Google Scholar 

  59. Buhan, I., Breebaart, J., Guajardo, J., de Groot, K., Kelkboom, E., Akkermans, T.: A quantitative analysis of indistinguishability for a continuous domain biometric cryptosystem. In: Proceedings of 1st International Workshop Signal Processing in the Encrypted Domain, pp. 82–99. SPEED, Lausanne (2009)

    Google Scholar 

  60. Buhan, I., Doumen, J., Hartel, P., Veldhuis, R.: Fuzzy extractors for continuous distributions. Technical report 06–72, University of Twente (2006)

    Google Scholar 

  61. Buhan, I., Doumen, J., Hartel, P., Veldhuis, R.: Constructing practical fuzzy extractors using qim. Technical report 07–52, University of Twente (2007)

    Google Scholar 

  62. Buhan, I., Guajardo, J., Kelkboom, E.: Efficient strategies to play the indistinguishability game for fuzzy sketches. In: Proceedings of IEEE Workshop on Information Forensics and Security, pp. 1–6. IEEE, New York (2010). doi: 10.1109/WIFS.2010.5711473

    Google Scholar 

  63. Burge, M., Bowyer, K.: Handbook of Iris Recognition. Springer, New York (2012)

    Google Scholar 

  64. Burl, M., Leung, T.K., Perona, P.: Face localization via shape statistics. In: Proceedings of International Workshop on Automatic Face and Gesture Recognition, pp. 154–159 (1995)

    Google Scholar 

  65. Camus, T., Wildes, R.: Reliable and fast eye finding in close-up images. In: Proceedings of 16th International Conference on Pattern Recognition, vol. 1, pp. 389–394. IEEE, New York (2002). doi: 10.1109/ICPR.2002.1044732

    Google Scholar 

  66. Canada Border Services Agency: NEXUS. URL http://www.cbsa-asfc.gc.ca/prog/nexus/menu-eng.html. Retrieved May 2012

  67. Cao, J.G., Fowler, J., Younan, N.: An image-adaptive watermark based on a redundant wavelet transform. In: Proceedings of IEEE International Conference on Image Processing, vol. 2, pp. 277–280. IEEE, New York (2001). doi: 10.1109/ICIP.2001.958478

    Google Scholar 

  68. Cauchie, J., Fiolet, V., Villers, D.: Optimization of an hough transform algorithm for the search of a center. Pattern Recogn. 41(2), 567–574 (2008). doi: 10.1016/j.patcog.2007.07.001

    MATH  Google Scholar 

  69. Cavoukian, A., Stoianov, A.: Biometric encryption. In: Encyclopedia of Biometrics. Springer, New York (2009)

    Google Scholar 

  70. Cavoukian, A., Stoianov, A.: Biometric encryption: The new breed of untraceable biometrics. In: Biometrics: Fundamentals, Theory, and Systems. Wiley, London (2009). doi: 10.1002/9780470522356.ch26

    Google Scholar 

  71. Center for Identification Technology Research, West Virginia University: Biometric Dataset Collection. URL http://www.citer.wvu.edu/biometric\_dataset\_collections. Retrieved May 2012

  72. Cha, S.H., Tappert, C., Yoon, S.: Enhancing binary feature vector similarity measures. J. Pattern Recogn. Res. 1(1), 63–77 (2006)

    Google Scholar 

  73. Chafia, F., Salim, C., Farid, B.: A biometric crypto-system for authentication. In: Proceedings of International Conference on Machine and Web Intelligence, pp. 434–438. IEEE, New York (2010). doi: 10.1109/ICMWI.2010.5648101

    Google Scholar 

  74. Chakraborty, A., Duncan, J.: Game-theoretic integration for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 21, 12–30 (1999). doi: 10.1109/34.745730

    Google Scholar 

  75. Chan, T., Vese, L.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001). doi: 10.1109/83.902291

    MATH  Google Scholar 

  76. Chang, E.C., Shen, R., Teo, F.: Finding the original point set hidden among chaff. In: Proceedings of 2006 ACM Symposium on Information, Computer and Communications Security, pp. 182–188. ACM, New York (2006). doi: 10.1145/1128817.1128845

    Google Scholar 

  77. Chen, B., Chandran, V.: Biometric based cryptographic key generation from faces. In: Proceedings of Digital Image Computing: Techniques and Applications, pp. 394–401. IEEE, New York (2007). doi: 10.1109/DICTA.2007.4426824

    Google Scholar 

  78. Chen, M., Fridrich, J., Goljan, M., Lukas, J.: Source digital camcorder identification using sensor photo-response non-uniformity. In: Delp, E., Wong, P. (eds.) Security, Steganography, and Watermarking of Multimedia Contents IX, Proceedings of SPIE, vol. 6505, pp. 65,051G.1–12. SPIE, Bellingham, WA (2007). doi: 10.1117/12.696519

    Google Scholar 

  79. Chen, M., Fridrich, J., Goljan, M., Lukas, J.: Determining image origin and integrity using sensor noise. IEEE Trans. Inform. Secur. Forensics 3(1), 74–90 (2008). doi: 10.1109/TIFS.2007.916285

    Google Scholar 

  80. Chen, M., Zhang, S., Karim, M.: Modification of standard image compression methods for correlation-based pattern recognition. Opt. Eng. 43(8), 1723–1730 (2004). doi: 10.1117/1.1765664

    Google Scholar 

  81. Chen, R., Lin, X., Ding, T.: Iris segmentation for non-cooperative recognition systems. IET Image Process. 5(5), 448–456 (2011). doi: 10.1049/iet-ipr.2009.0234

    Google Scholar 

  82. Chen, T.S., Chen, J., Chen, J.G.: A simple and efficient watermark technique based on JPEG2000 codec. Multimed. Syst. 10(1), 16–26 (2004). doi: 10.1007/s00530-004-0133-8

    Google Scholar 

  83. Chen, W.S., Chih, K.H., Shih, S.W., Hsieh, C.M.: Personal identification with human iris recognition based on wavelet transform. In: Proceedings of IAPR Conference on Machine Vision Applications, pp. 351–354 (2005)

    Google Scholar 

  84. Chen, W.S., Yuan, S.Y.: A novel personal biometric authentication technique using human iris based on fractal dimension features. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 3, pp. 201–204 (2003). doi: 10.1109/ICASSP.2003.1199142

    Google Scholar 

  85. Chen, Y., Adjouadi, M., Barreto, A., Rishe, N., Andrian, J.: A computational efficient iris extraction approach in unconstrained environments. In: Proceedings of IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–7. IEEE, New York (2009). doi: 10.1109/BTAS.2009.5339024

    Google Scholar 

  86. Chen, Y., Adjouadi, M., Han, C., Wang, J., Barreto, A., Rishe, N., Andrian, J.: A highly accurate and computationally efficient approach for unconstrained iris segmentation. Image Vis. Comput. 28(2), 261–269 (2010). doi: 10.1016/j.imavis.2009.04.017

    Google Scholar 

  87. Chen, Y., Dass, S.C., Jain, A.K.: Localized iris image quality using 2-d wavelets. In: Zhang, D., Jain, A. (eds.) Proceedings of 1st International Conference on Biometrics. LNCS, vol. 3832, pp. 373–381. Springer, New York (2006). doi: 10.1007/11608288_50

    Google Scholar 

  88. Chen, Y., Wang, J., Han, C., Wang, L., Adjouadi, M.: A robust segmentation approach to iris recognition based on video. In: Proceedings of 37th Applied Imagery Pattern Recognition Workshop, pp. 1–8. IEEE, New York (2008). doi: 10.1109/AIPR.2008.4906441

    Google Scholar 

  89. Chinese Academy of Sciences, Institute of Automation: Biometrics Ideal Test. URL http://biometrics.idealtest.org. Retrieved May 2012

  90. Chinese Academy of Sciences, Institute of Automation: Service Note on CASIA Iris Image Databases. URL http://www.cbsr.ia.ac.cn/HFB\_Agreement/ServiceNoteOnCasiaIrisImageDatabases.pdf. Retrieved May 2012

  91. Cho, D., Park, K., Rhee, D., Kim, Y., Yang, J.: Pupil and iris localization for iris recognition in mobile phones. In: Proceedings of 7th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, pp. 197–201. IEEE, New York (2006). doi: 10.1109/SNPD-SAWN.2006.58

    Google Scholar 

  92. Choi, S., Cha, S., Tappert, C.: A survey of binary similarity and distance measures. J. Systemics Cybern. Informat. 8(1), 43–48 (2010)

    Google Scholar 

  93. Chong, S.C., Jin, A.T.B., Ling, D.N.C.: High security iris verification system based on random secret integration. Comput. Vis. Image Understand. 102(2), 169–177 (2006). doi: 10.1016/j.cviu.2006.01.002

    Google Scholar 

  94. Chong, S.C., Jin, A.T.B., Ling, D.N.C.: Iris authentication using privatized advanced correlation filter. In: Zhang, D., Jain, A. (eds.) Proceedings of 1st International Conference on Biometrics. LNCS, vol. 3832, pp. 382–388. Springer, New York (2006). doi: 10.1007/11608288_51

    Google Scholar 

  95. Chou, C.T., Shih, S.W., Chen, W.S., Cheng, V.W.: Iris recognition with multi-scale edge-type matching. In: Proceedings of 18th International Conference on Pattern Recognition, pp. 545–548. IEEE, New York (2006). doi: 10.1109/ICPR.2006.728

    Google Scholar 

  96. Chu, C.T., Chen, C.H.: High performance iris recognition based on lda and lpcc. In: Proceedings of 17th IEEE International Conference on Tools with Artificial Intelligence, pp. 417–421. IEEE, New York (2005)

    Google Scholar 

  97. Chung, Y., Moon, D., Lee, S., Jung, S., Kim, T., Ahn, D.: Automatic alignment of fingerprint features for fuzzy fingerprint vault. In: Proceedings of Conference on Information Security and Cryptology. LNCS, vol. 3822, pp. 358–369. Springer, New York (2005). doi: 10.1007/11599548_31

    Google Scholar 

  98. Chung, Y., Moon, D., Moon, K., Pan, S.: Hiding biometric data for secure transmission. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds.) Proceedings of KES 2005. LNCS, vol. 3683, pp. 1049–1057. Springer, New York (2005). doi: 10.1007/11553939_147

    Google Scholar 

  99. Cimato, S., Gamassi, M., Piuri, V., Sassi, R., Scotti, F.: A multi-biometric verification system for the privacy protection of iris templates. In: Corchado, E., Zunino, R., Gastaldo, P., Herrero, Á. (eds.) Proceedings of International Workshop on Computational Intelligence in Security for Information Systems, Advances in Soft Computing, vol. 53, pp. 227–234. Springer, New York (2008). doi: 10.1007/978-3-540-88181-0_29

    Google Scholar 

  100. Clancy, T.C., Kiyavash, N., Lin, D.J.: Secure smartcard-based fingerprint authentication. In: Proceedings of ACM 2003 Multimedia, Biometrics Methods and Applications Workshop, pp. 45–52. ACM, New York (2003). doi: 10.1145/982507.982516

    Google Scholar 

  101. Cogent 3M: CIS 202. URL http://www.cogentsystems.com/cis202.asp. Retrieved June 2012

  102. Commission Int’l de l’Eclairage, TC 6-38: Photobiological Safety Standards for Safety Standards for Lamps, CIE 134-3-99 (1999)

    Google Scholar 

  103. Computer Vision Research Laboratory: CVRL Data Sets. URL http://www.nd.edu/~cvrl/CVRL/Data\_Sets.html. Retrieved May 2012

  104. Connie, T., Teoh, A., Goh, M., Ngo, D.: Palmhashing: a novel approach for cancelable biometrics. Inform. Process. Lett. 93(1), 1–5 (2005). doi: 10.1016/j.ipl.2004.09.014

    MathSciNet  Google Scholar 

  105. Cristinacce, D., Cootes, T., Scott, I.: A multi-stage approach to facial feature. In: Proceedings of British Machine Vision Conference, pp. 231–240. BMVA Press, Guildford (2004)

    Google Scholar 

  106. Crossmatch: I SCAN 2. URL http://www.crossmatch.com/de/i-scan-2.php. Retrieved June 2012

  107. Cui, J., Wang, Y., Tan, T., Ma, L., Sun, Z.: A fast and robust iris localization method based on texture segmentation. In: Jain, A., Ratha, N. (eds.) Biometric Technology for Human Identification, Proceedings of SPIE, vol. 5404, pp. 401–408. SPIE, Bellingham, WA (2004). doi: 10.1117/12.541921

    Google Scholar 

  108. Daugman, J.: Two-dimensional spectral analysis of cortical receptive field profiles. Vis. Res. 20(10), 847–856 (1980). doi: 10.1016/0042-6989(80)90065-6

    Google Scholar 

  109. Daugman, J.: High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1148–1161 (1993). doi: 10.1109/34.244676

    Google Scholar 

  110. Daugman, J.: Biometric personal identification system based on iris analysis (1994). US Patent 5291560, WIPO Patent 9409446

    Google Scholar 

  111. Daugman, J.: Biometric decision landscapes. Technical report tr482, Cambridge University (2000)

    Google Scholar 

  112. Daugman, J.: Iris recognition. Am. Sci. 89(4), 326–333 (2001). doi: 10.1511/2001.4.326

    Google Scholar 

  113. Daugman, J.: Statistical richness of visual phase information: Update on recognizing persons by iris patterns. Int. J. Comput. Vis. 45(1), 25–38 (2001). doi: 10.1023/A:1012365806338

    MATH  Google Scholar 

  114. Daugman, J.: How the afghan girl was identified by her iris patterns (2002). URL http://www.cl.cam.ac.uk/~jgd1000/afghan.html. Retrieved May 2012

  115. Daugman, J.: The importance of being random: statistical principles of iris recognition. Pattern Recogn. 36(2), 279–291 (2003). doi: 10.1016/S0031-3203(02)00030-4

    Google Scholar 

  116. Daugman, J.: How iris recognition works. IEEE Trans. Circ. Syst. Video Tech. 14(1), 21–30 (2004). doi: 10.1109/ICIP.2002.1037952

    Google Scholar 

  117. Daugman, J.: Probing the uniqueness and randomness of iriscodes: Results from 200 billion iris pair comparisons. Proc. IEEE 94(11), 1927–1935 (2006). doi: 10.1109/JPROC.2006.884092

    Google Scholar 

  118. Daugman, J.: New methods in iris recognition. IEEE Trans. Syst. Man Cybern. B Cybern. 37(5), 1167–1175 (2007). doi: 10.1109/TSMCB.2007.903540

    Google Scholar 

  119. Daugman, J.: Iris recognition at airports and border-crossings. In: Encyclopedia of Biometrics. Springer, New York (2009)

    Google Scholar 

  120. Daugman, J., Downing, C.: Epigenetic randomness, complexity and singularity of human iris patterns. In: Proceedings of the Royal Society (London): Biological Sciences, vol. 268, pp. 1737–1740. The Royal Society, London (2001)

    Google Scholar 

  121. Daugman, J., Downing, C.: Effect of severe image compression on iris recognition performance. IEEE Trans. Inform. Forensics Secur. 3, 52–61 (2008). doi: 10.1109/ TIFS.2007.916009

    Google Scholar 

  122. Daugman, J., Malhas, I.: Iris recognition border-crossing system in the uae. Int. Airport Rev. 2, 49–53 (2004)

    Google Scholar 

  123. Daugman, J.G.: Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J. Opt. Soc. Am. A Opt. Image Sci. Vis. 2(7), 1160–1169 (1985). doi: 10.1364/JOSAA.2.001160

    Google Scholar 

  124. Davida, G., Frankel, Y., Matt, B.: On enabling secure applications through off-line biometric identification. In: Proceedings of IEEE Symposium on Security and Privacy, pp. 148–157. IEEE, New York (1998). doi: 10.1109/SECPRI.1998.674831

    Google Scholar 

  125. Davida, G., Frankel, Y., Matt, B.: On the relation of error correction and cryptography to an off line biometric based identication scheme. In: Proceedings of Workshop on Coding and Cryptography, pp. 129–138 (1999)

    Google Scholar 

  126. De Marsico, M., Nappi, M., Daniel, R.: Isis: Iris segmentation for identification systems. In: Proceedings of 20th International Conference on Pattern Recognition, pp. 2857–2860. IEEE, New York (2010). doi: 10.1109/ICPR.2010.700

    Google Scholar 

  127. Delivasilis, D.L., Katsikas, S.K.: Side channel analysis on biometric-based key generation algorithms on resource constrained devices. Int. J. Netw. Secur. 3(1), 44–50 (2005)

    Google Scholar 

  128. Delvaux, N., Chabanne, H., Bringer, J., Kindarji, B., Lindeberg, P., Midgren, J., Breebaart, J., Akkermans, T., van der Veen, M., Veldhuis, R., Kindt, E., Simoens, K., Busch, C., Bours, P., Gafurov, D., Yang, B., Stern, J., Rust, C., Cucinelli, B., Skepastianos, D.: Pseudo identities based on fingerprint characteristics. In: Proceedings of International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 1063–1068. IEEE, New York (2008). doi: 10.1109/IIH-MSP.2008.327

    Google Scholar 

  129. Determan, G.E., Jacobson, V.C., Jelinek, J., Phinney, T., Hamza, R.M., Ahrens, T., Kilgore, G.A., Whillock, R.P., Bedros, S.: Combined face and iris recognition system (2007). U.S. Patent 20080075334

    Google Scholar 

  130. Ding, S., Li, C., Liu, Z.: Protecting hidden transmission of biometrics using authentication watermarking. In: Proceedings of 2010 WASE International Conference on Information Engineering, pp. 105–108. IEEE, New York (2010). doi: 10.1109/ICIE.2010.120

    Google Scholar 

  131. Dobes, M., Martinek, J., Skoupil, D., Dobesova, Z., Pospisil, J.: A robust method for eye features extraction on color image. Optik 117(10), 468–473 (2005). doi: 10.1016/j.patrec.2005.03.033

    Google Scholar 

  132. Dodis, Y., Ostrovsky, R., Reyzin, L., Smith, A.: Fuzzy Extractors: How to Generate Strong Keys from Biometrics and Other Noisy Data. In: Cachin, C., Camenisch, J. (eds.) Proceedings of International Conference on Theory And Applications of Cryptographic Techniques. LNCS, vol. 3027, pp. 523–540. Springer, New York (2004). doi: 10.1007/978-3-540-24676-3_31

    Google Scholar 

  133. Dong, J., Tan, T.: Effects of watermarking on iris recognition performance. In: Proceedings of 10th International Conference on Control, Automation, Robotics and Vision, pp. 1156–1161. IEEE, New York (2008). doi: 10.1109/ICARCV.2008.4795684

    Google Scholar 

  134. Dorairaj, V., Schmid, N.A., Fahmy, G.: Performance evaluation of iris-based recognition system implementing pca and ica encoding techniques. In: Jain, A.K., Ratha, N.K. (eds.) Biometric Technology for Human Identification II, Proceedings of SPIE, vol. 5779, pp. 51–58. SPIE, Bellingham, WA (2005). doi: 10.1117/12.604201

    Google Scholar 

  135. Du, Y.: Using 2D log-Gabor spatial filters for iris recognition. In: Flynn, P.J., Pankanti, S. (eds.) Biometric Technology for Human Identification III, Proceedings of SPIE, vol. 6202, pp. 62,020F.1–8. SPIE, Bellingham, WA (2006). doi: 10.1117/12.663834

    Google Scholar 

  136. Du, Y., Arslanturk, E., Zhou, Z., Belcher, C.: Video-based noncooperative iris image segmentation. IEEE Trans. Syst. Man Cybern. B Cybern. 41(1), 64–74 (2011). doi: 10.1109/TSMCB.2010.2045371

    Google Scholar 

  137. Du, Y., Ives, R., Etter, D., Welch, T.: A new approach to iris pattern recognition. In: Driggers, R.G., Huckridge, D.A. (eds.) Electro-Optical and Infrared Systems: Technology and Applications, Proceedings of SPIE, vol. 5612, pp. 104–116. SPIE, Bellingham, WA (2004). doi: 10.1117/12.578789

    Google Scholar 

  138. Dufaux, F., Sullivan, G.J., Ebrahimi, T.: The JPEG XR image coding standard. IEEE Signal. Process. Mag. 26(6), 195–199 (2009). doi: 10.1109/MSP.2009.934187

    Google Scholar 

  139. Dugad, R., Ratakonda, K., Ahuja, N.: A new wavelet-based scheme for watermarking images. In: Proceedings of IEEE International Conference on Image Processing, vol. 2, pp. 419–423. IEEE, New York (1998). doi: 10.1109/ICIP.1998.723406

    Google Scholar 

  140. Ekici, Ö., Sankur, B., Akcay, M.: Comparative evaluation of semi-fragile watermarking algorithms. J. Electron. Imag. 13(1), 209–216 (2003). doi: 10.1117/1.1633285

    Google Scholar 

  141. Failla, P., Sutcu, Y., Barni, M.: esketch: a privacy-preserving fuzzy commitment scheme for authentication using encrypted biometrics. In: Proceedings of 12th Workshop on Multimedia and Security, pp. 241–246. ACM, New York (2010). doi: 10.1145/1854229.1854271

    Google Scholar 

  142. Fancourt, C., Bogoni, L., Hanna, K., Guo, Y., Wildes, R., Takahashi, N., Jain, U.: Iris recognition at a distance. In: Kanade, T., Jain, A., Ratha, N. (eds.) Proceedings of 5th International Conference on Audio- and Video-Based Biometric Person Authentication. LNCS, vol. 3546, pp. 1–13. Springer, New York (2005). doi: 10.1007/11527923_1

    Google Scholar 

  143. Färberböck, P., Hämmerle-Uhl, J., Kaaser, D., Pschernig, E., Uhl, A.: Transforming rectangular and polar iris images to enable cancelable biometrics. In: Campilho, A., Kamel, M. (eds.) Proceedings of 7th International Conference on Image Analysis and Recognition. LNCS, vol. 6112, pp. 276–386. Springer, New York (2010). doi: 10.1007/978-3-642-13775-4_28

    Google Scholar 

  144. Farid, H.: Image forgery detection. IEEE Signal. Process. Mag. 26(2), 16–25 (2009). doi: 10.1109/MSP.2008.931079

    Google Scholar 

  145. fCoder Group: ImageConverter Plus. URL http://www.imageconverterplus.com/. Retrieved June 2012

  146. Feng, H., Wah, C.C.: Private key generation from on-line handwritten signatures. Inform. Manag. Comput. Secur. 10, 159–164 (2002). doi: 10.1108/09685220210436949

    Google Scholar 

  147. Feng, J.B., Lin, I.C., Tsai, C.S., Chu, Y.P.: Reversible watermarking: current status and key issues. Int. J. Netw. Secur. 2(3), 161–171 (2006)

    Google Scholar 

  148. Feng, Y.C., Yuen, P.C., Jain, A.K.: A hybrid approach for face template protection. In: Kumar, B.V., Prabhakar, S., Ross, A.A. (eds.) Biometric Technology for Human Identification V, Proceedings of SPIE, vol. 6944, pp. 694,408.1–11. SPIE, Bellingham, WA (2008). doi: 10.1117/12.778652

    Google Scholar 

  149. Fenker, S.P., Bowyer, K.W.: Experimental evidence of a template aging effect in iris biometrics. In: Proceedings of IEEE Workshop on Applications of Computer Vision, pp. 232–239. IEEE, New York (2011). doi: 10.1109/WACV.2011.5711508

    Google Scholar 

  150. Filho, C., Costa, M.: Iris segmentation exploring color spaces. In: Proceedings of 3rd International Conference on Image and Signal Processing, vol. 4, pp. 1878–1882. IEEE, New York (2010). doi: 10.1109/CISP.2010.5647406

    Google Scholar 

  151. Fitzgibbon, A., Pilu, M., Fisher, R.B.: Direct least square fitting of ellipses. IEEE Trans. Pattern Anal. Mach. Intell. 21(5), 476–480 (1999). doi: 10.1109/34.765658

    Google Scholar 

  152. Flom, L., Safir, A.: Iris recognition system (1986). US Patent 4641349, WIPO Patent 8605018

    Google Scholar 

  153. Flynn, P.J.: Biometrics databases. In: Jain, A.K., Flynn, P., Ross, A.A. (eds.) Handbook of Biometrics, pp. 529–548. Springer, New York (2008)

    Google Scholar 

  154. Fong, W., Chan, S., Ho, K.: Designing JPEG quantization matrix using rate-distortion approach and human visual system model. In: Proceedings of IEEE International Conference on Communications, vol. 3, pp. 1659–1663. IEEE, New York (1997). doi: 10.1109/ICC.1997.595069

    Google Scholar 

  155. Forrester, J.V., Dick, A.D., McMenamin, P.G., Roberts, F.: The Eye: Basic Sciences in Practice, 3rd edn. Saunders, Philadelphia, PA (2008)

    Google Scholar 

  156. Fridrich, J.: Digital image forensic using sensor noise. IEEE Signal. Process. Mag. 26(2) (2009)

    Google Scholar 

  157. Frost, A.: FuturixImager. URL http://fximage.com/. Retrieved June 2012

  158. Gaddam, S.V.K., Lal, M.: Effcient cancellable biometric key generation scheme for cryptography. Int. J. Netw. Secur. 11(2), 61–69 (2010)

    Google Scholar 

  159. Gan, J.Y., Liang, Y.: A method for face and iris feature fusion in identity authentication. Int. J. Comp. Sci. Netw. Secur. 6(2), 135–138 (2006)

    Google Scholar 

  160. Gentile, J.E., Ratha, N., Connell, J.: An efficient, two-stage iris recognition system. In: Proceedings of IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–5. IEEE, New York (2009). doi: 10.1109/BTAS.2009.5339056

    Google Scholar 

  161. Gentile, J.E., Ratha, N., Connell, J.: Slic: Short-length iris codes. In: Proceedings of IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–5. IEEE, New York (2009). doi: 10.1109/BTAS.2009.5339027

    Google Scholar 

  162. Georghiades, A.S., Belhumeur, P.N.: From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 643–660 (2001). doi: 10.1109/34.927464

    Google Scholar 

  163. Goh, A., Ngo, D.C.L.: Computation of cryptographic keys from face biometrics. In: Lioy, A., Mazzocchi, D. (eds.) Proceedings of 7th International Conference on Communications and Multimedia Security. LNCS, vol. 2828, pp. 1–13. Springer, New York (2003). doi: 10.1007/978-3-540-45184-6_1

    Google Scholar 

  164. Goh, A., Teoh, A.B.J., Ngo, D.C.L.: Random multispace quantization as an analytic mechanism for biohashing of biometric and random identity inputs. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 1892–1901 (2006). doi: 10.1109/TPAMI.2006.250

    Google Scholar 

  165. Goljan, M., Fridrich, J., Chen, M.: Sensor noise camera identification: Countering counter-forensics. In: Memon, N.D., Dittmann, J., Alattar, A.M., Delp, E.J. (eds.) Media Forensics and Security II, Proceedings of SPIE, vol. 7541, pp. 75,410S.1–12. SPIE, Bellingham, WA (2010). doi: 10.1117/12.839055

    Google Scholar 

  166. Goljan, M., Fridrich, J., Chen, M.: Defending against fingerprint-copy attack in sensor-based camera identification. IEEE Trans. Inform. Secur. Forensics 6(1), 227–236 (2011). doi: 10.1109/TIFS.2010.2099220

    Google Scholar 

  167. Goljan, M., Fridrich, J., Filler, T.: Large scale test of sensor fingerprint camera identification. In: Delp, E.J., Dittmann, J., Memon, N.D., Wong, P.W. (eds.) Electronic Imaging, Security and Forensics of Multimedia Contents XI, Proceedings of SPIE, vol. 7254, pp. 72540I.1–12. SPIE, Bellingham, WA (2009). doi: 10.1117/12.805701

    Google Scholar 

  168. Goljan, M., Fridrich, J., Filler, T.: Managing a large database of camera fingerprints. In: Memon, N.D., Dittmann, J., Alattar, A.M., Delp, E.J. (eds.) Media Forensics and Security II, Proceedings of SPIE, vol. 7541, pp. 754,108.1–12. SPIE, Bellingham, WA (2010). doi: 10.1117/12.838378

    Google Scholar 

  169. Gong, Y., Deng, K., Shi, P.: Pki key generation based on iris features. In: Proceedings of International Conference on Computer Science and Software Engineering, vol. 6, pp. 166–169. IEEE, New York (2008). doi: 10.1109/CSSE.2008.1181

    Google Scholar 

  170. Gougelet, P.E.: XN-View. URL http://www.xnview.com/. Retrieved June 2012

  171. Grabowski, K., Napieralski, A.: Hardware architecture optimized for iris recognition. IEEE Trans. Circ. Syst. Video Tech. 21(9), 1293–1303 (2011). doi: 10.1109/TCSVT.2011.2147150

    Google Scholar 

  172. Grother, P., Quinn, G.W., Matey, J.R., Ngan, M., Salamon, W., Fiumara, G., Watson, C.: Irex iii performance of iris identification algorithms. Interagency report 7836, NIST (2012)

    Google Scholar 

  173. Grother, P., Tabasi, E., Quinn, G.W., Salamon, W.: Irex i performance of iris recognition algorithms on standard images. Interagency report 7629, NIST (2009)

    Google Scholar 

  174. Gunsel, B., Uludag, U., Tekalp, A.M.: Robust watermarking of fingerprint images. Pattern Recogn. 35(12), 2739–2747 (2002). doi: 10.1016/S0031-3203(01)00250-3

    MATH  Google Scholar 

  175. Gupta, P., Sana, A., Mehrotra, H., Hwang, C.J.: An efficient indexing scheme for binary feature based biometric database. In: Prabhakar, S., Ross, A. (eds.) Biometric Technology for Human Identification IV, Proceedings of SPIE, vol. 6539, pp. 653,909.1–10. SPIE, Bellingham, WA (2007). doi: 10.1117/12.719237

    Google Scholar 

  176. Hämmerle-Uhl, J., Karnutsch, M., Uhl, A.: Recognition impact of JPEG2000 part 2 wavelet packet subband structures in polar iris image compression. In: Zovko-Cihlar, B., Rupp, M., Mecklenbräuker, C. (eds.) Proceedings of 19th International Conference on Systems, Signals and Image Processing, pp. 13–16. IEEE, New York (2012)

    Google Scholar 

  177. Hämmerle-Uhl, J., Prähauser, C., Starzacher, T., Uhl, A.: Improving compressed iris recognition accuracy using jpeg2000 roi coding. In: Tistarelli, M., Nixon, M. (eds.) Proceedings of 3rd International Conference on Biometrics. LNCS, vol. 5558, pp. 1102–1111. Springer, New York (2009). doi: 10.1007/978-3-642-01793-3_111

    Google Scholar 

  178. Hämmerle-Uhl, J., Pschernig, E., Uhl, A.: Cancelable iris biometrics using block re-mapping and image warping. In: Samarati, P., Yung, M., Martinelli, F., Ardagna, C. (eds.) Proceedings of 12th International Information Security Conference. LNCS, vol. 5735, pp. 135–142. Springer, New York (2009). doi: 10.1007/978-3-642-04474-8_11

    Google Scholar 

  179. Hämmerle-Uhl, J., Raab, K., Uhl, A.: Experimental study on the impact of robust watermarking on iris recognition accuracy. In: Proceedings of 25th ACM Symposium on Applied Computing, pp. 1479–1484. ACM, New York (2010). doi: 10.1145/1774088.1774405

    Google Scholar 

  180. Hämmerle-Uhl, J., Raab, K., Uhl, A.: Attack against robust watermarking-based multimodal biometric recognition systems. In: Vielhauer, C., Dittmann, J., Drygajlo, A., Juul, N., Fairhurst, M. (eds.) Proceedings of COST 2101 European Workshop on Biometrics and ID Management. LNCS, vol. 6583, pp. 25–36. Springer, New York (2011). doi: 10.1007/978-3-642-19530-3_3

    Google Scholar 

  181. Hämmerle-Uhl, J., Raab, K., Uhl, A.: Robust watermarking in iris recognition: application scenarios and impact on recognition performance. ACM SIGAPP Appl. Comput. Rev. 11(3), 6–18 (2011). doi: 10.1145/2034594.2034595

    Google Scholar 

  182. Hämmerle-Uhl, J., Raab, K., Uhl, A.: Watermarking as a means to enhance biometric systems: A critical survey. In: Filler, T., Pevny, T., Craver, S., Ker, A. (eds.) Proceedings of 13th International Conference on Information Hiding. LNCS, vol. 6958, pp. 238–254. Springer, New York (2011). doi: 10.1007/978-3-642-24178-9_17

    Google Scholar 

  183. Han, B.J., Shin, Y.N., Jeun, I.K., Jung, H.C.: A framework for alternative pin service based on cancellable biometrics. In: Proceedings of Joint Workshop on Information Security, pp. 1–10 (2009)

    Google Scholar 

  184. Hao, F., Anderson, R., Daugman, J.: Combining Cryptography with Biometrics Effectively. IEEE Trans. Comput. 55(9), 1081–1088 (2006). doi: 10.1109/TC.2006.138

    Google Scholar 

  185. Hassanien, A.E., Abraham, A., Grosan, C.: Spiking neural network and wavelets for hiding iris data in digital images. Soft Comput. Fusion Foundations Methodologies Appl. 13(4), 401–416 (2008). doi: 10.1007/s00500-008-0324-x

    Google Scholar 

  186. He, X., Shi, P.: A new segmentation approach for iris recognition based on hand-held capture device. Pattern Recogn. 40, 1326–1333 (2007). doi: 10.1016/j.patcog.2006.08.009

    MATH  Google Scholar 

  187. He, Y., Liu, T., Hou, Y., Wang, Y.: A fast iris image quality evaluation method based on weighted entropy. In: Zhou, L. (ed.) International Symposium on Photoelectronic Detection and Imaging, Proceedings of SPIE, vol. 6623, pp. 66,231R.1–8. SPIE, Bellingham, WA (2007). doi: 10.1117/12.791526

    Google Scholar 

  188. He, Z., Tan, T., Sun, Z.: Iris localization via pulling and pushing. In: Proceedings of 18th International Conference on Pattern Recognition, vol. 4, pp. 366–369. IEEE, New York (2006). doi: 10.1109/ICPR.2006.724

    Google Scholar 

  189. He, Z., Tan, T., Sun, Z., Qiu, X.: Toward accurate and fast iris segmentation for iris biometrics. IEEE Trans. Pattern Anal. Mach. Intell. 31(9), 1670–1684 (2009). doi: 10.1109/TPAMI.2008.183

    Google Scholar 

  190. Hernandez, A., Hernandez, E., Sanchez, A.: Biometric fuzzy extractor scheme for iris templates. In: Proceedings of International Conference on Security and Management, vol. 2, pp. 563–569 (2009)

    Google Scholar 

  191. Hirata, S., Takahashi, K.: Cancelable biometrics with perfect secrecy for correlation-based matching. In: Tistarelli, M., Nixon, M. (eds.) Proceedings of 3rd International Conference on Biometrics. LNCS, vol. 5558, pp. 868–878. Springer, New York (2009). doi: 10.1007/978-3-642-01793-3_88

    Google Scholar 

  192. Hoang, T., Tran, D., Sharma, D.: Remote multimodal biometric authentication using bit priority-based fragile watermarking. In: Proceedings of 19th International Conference on Pattern Recognition, pp. 1–4. IEEE, New York (2008). doi: 10.1109/ICPR.2008.4761869

    Google Scholar 

  193. Hofbauer, H., Rathgeb, C., Uhl, A., Wild, P.: Image metric-based biometric comparators: a supplement to feature vector-based hamming distance? In: Proceedings of IEEE International Conference of the Biometrics Special Interest Group, LNI, vol. 196. GI, Bonn (2012)

    Google Scholar 

  194. Hofbauer, H., Rathgeb, C., Uhl, A., Wild, P.: Iris recognition in image domain: Quality-metric based comparators. In: G. Bebis et al. (eds.) Proceedings of 8th International Symposium on Visual Computing. LNCS, vol. 7432, pp. 1–10. Springer, New York (2012). doi: 10.1007/978-3-642-33191-6_1

    Google Scholar 

  195. Hofbauer, H., Uhl, A.: An effective and efficient visual quality index based on local edge gradients. In: Proceedings of IEEE 3rd European Workshop on Visual Information Processing, pp. 162–167. IEEE, New York (2011). doi: 10.1109/EuVIP.2011.6045514

    Google Scholar 

  196. Hollingsworth, K.P., Bowyer, K.W., Flynn, P.J.: The best bits in an iris code. IEEE Trans. Pattern Anal. Mach. Intell. 31(6), 964–973 (2009). doi: 10.1109/TPAMI.2008.185

    Google Scholar 

  197. Hong, S., Jeon, W., Kim, S., Won, D., Park, C.: The vulnerabilities analysis of fuzzy vault using password. In: Proceedings of 2nd International Conference on Future Generation Communication and Networking, pp. 76–83. IEEE, New York (2008). doi: 10.1109/FGCN.2008.211

    Google Scholar 

  198. Hong, S., Kim, H., Lee, S., Chung, Y.: Analyzing the secure and energy efficient transmissions of compressed fingerprint images using encryption and watermarking. In: Proceedings of International Conference on Information Security and Assurance, pp. 316–320. IEEE, New York (2008). doi: 10.1109/ISA.2008.57

    Google Scholar 

  199. Hoque, S., Fairhurst, M., Howells, G.: Evaluating biometric encryption key generation using handwritten signatures. In: Proceedings of the 2008 Bio-inspired, Learning and Intelligent Systems for Security, pp. 17–22. IEEE, New York (2008). doi: 10.1109/BLISS.2008.8

    Google Scholar 

  200. Horvath, K., Stögner, H., Uhl, A.: Effects of jpeg xr compression settings on iris recognition systems. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds.) Proceedings of 14th International Conference on Computer Analysis of Images and Patterns. LNCS, vol. 6855, pp. 73–80. Springer, New York (2011). doi: 10.1007/978-3-642-23678-5_7

    Google Scholar 

  201. Horvath, K., Stögner, H., Uhl, A.: Optimisation of JPEG XR quantisation settings in iris recognition systems. In: Davies, P., Newell, D. (eds.) Proceedings of 4th International Conference on Advances in Multimedia, pp. 88–93. IARIA, Wilmington, DE (2012)

    Google Scholar 

  202. Horvath, K., Stögner, H., Uhl, A., Weinhandel, G.: Experimental study on lossless compression of biometric iris data. In: Proceedings of 7th International Symposium on Image and Signal Processing and Analysis, pp. 379–384. IEEE, New York (2011)

    Google Scholar 

  203. Horvath, K., Stögner, H., Uhl, A., Weinhandel, G.: Lossless compression of polar iris image data. In: Vitria, J., Sanches, J., Hernandez, M. (eds.) Proceedings of 5th Iberian Conference on Pattern Recognition and Image Analysis. LNCS, vol. 6669, pp. 329–337. Springer, New York (2011). doi: 10.1007/978-3-642-21257-4_41

    Google Scholar 

  204. Hosseini, S.M., Araabi, B.N., Soltanian-Zadeh, H.: Shape analysis of stroma for iris recognition. In: Lee, S.W., Li, S. (eds.) Proceedings of 2nd International Conference on Biometrics. LNCS, vol. 4642, pp. 790–799. Springer, New York (2007). doi: 10.1007/978-3-540-74549-5_83

    Google Scholar 

  205. Hough, P.V.C.: Machine Analysis of Bubble Chamber Pictures. In: Proceedings of International Conference on High Energy Accelerators and Instrumentation. CERN, Geneva (1959)

    Google Scholar 

  206. Huang, J., Wang, Y., Tan, T., Cui, J.: A new iris segmentation method for recognition. In: Proceedings of 17th International Conference on Pattern Recognition, vol. 3, pp. 554–557. IEEE, New York (2004). doi: 10.1109/ICPR.2004.1334589

    Google Scholar 

  207. Huang, Y.P., Luo, S.W., Chen, E.Y.: An efficient iris recognition system. In: Proceedings of 1st International Conference on Machine Learning and Cybernetics, pp. 450–454. IEEE, New York (2002). doi: 10.1109/ICMLC.2002.1176794

    Google Scholar 

  208. Huber, R., Stögner, H., Uhl, A.: Semi-fragile watermarking in biometric systems: template self-embedding. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds.) Proceedings of the 14th International Conference on Computer Analysis of Images and Patterns (CAIP 2011). LNCS, vol. 6855, pp. 34–41. Springer, New York (2011)

    Google Scholar 

  209. Huber, R., Stögner, H., Uhl, A.: Two-factor biometric recognition with integrated tamper-protection watermarking. In: DeDecker, B., Lapon, J., Naessens, V., Uhl, A. (eds.) Proceedings of the 12th IFIP TC6/TC11 International Conference on Communications and Multimedia Security (CMS 2011). LNCS, vol. 7025, pp. 72–84. Springer, New York (2011)

    Google Scholar 

  210. Hui, L., Yu-ping, H.: Wavelet tree quantization-based biometric watermarking for offline handwritten signature. In: Proceedings of International Asia Symposium on Intelligent Interaction and Affective Computing, pp. 71–74. IEEE, New York (2009). doi: 10.1109/ASIA.2009.19

    Google Scholar 

  211. Ignatenko, T., Willems, F.: Achieving secure fuzzy commitment scheme for optical pufs. In: Proceedings of International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 1185–1188. IEEE, New York (2009). doi: 10.1109/IIH-MSP.2009.310

    Google Scholar 

  212. Ignatenko, T., Willems, F.M.J.: Information leakage in fuzzy commitment schemes. IEEE Trans. Inform. Forensics Secur. 5(2), 337–348 (2010). doi: 10.1109/TIFS.2010.2046984

    Google Scholar 

  213. ImageMagick Studio LLC: ImageMagick. URL http://www.imagemagick.org/. Retrieved June 2012

  214. Indian Institute of Technology Delhi: IIT Delhi Iris Database. URL http://www4.comp.polyu.edu.hk/~csajaykr/IITD/Database\_Iris.htm. Retrieved May 2012

  215. Intel, Willow Garage: Open Source Computer Vision Library. URL http://opencv.willowgarage.com. Retrieved May 2012

  216. Iris ID: iCAM 4000. URL http://www.irisid.com/icam4000series. Retrieved May 2012

  217. Iris ID: iCAM TD100. URL http://www.irisid.com/icamtd100. Retrieved May 2012

  218. Iris ID: IrisAccess in Action. URL http://www.irisid.com/irisaccessinaction. Retrieved May 2012

  219. IrisGuard: IG-AD100. URL http://www.irisguard.com/uploads/AD100ProductSheet.pdf. Retrieved May 2012

  220. IrisKing Ltd. Co.: IKEMB-100. URL http://www.irisking.com/en/sdk.html. Retrieved May 2012

  221. Ives, R., Bonney, B., Etter, D.: Effect of image compression on iris recognition. In: Proceedings of Conference on Instrumentation and Measurement Technology, pp. 2054–2058. IEEE, New York (2005). doi: 10.1109/IMTC.2005.1604535

    Google Scholar 

  222. Ives, R.W., Bishop, D.A., Du, Y., Belcher, C.: Iris recognition: The consequences of image compression. EURASIP J. Adv. Signal Process. 2010, 1–9 (2010). doi: 10.1155/2010/680845

    Google Scholar 

  223. Ives, R.W., Broussard, R.P., Kennell, L.R., Soldan, D.L.: Effects of image compression on iris recognition system performance. J. Electron. Imag. 17, 011015.1–8 (2008). doi: 10.1117/1.2891313

    Google Scholar 

  224. Ives, R.W., Guidry, A.J., Etter., D.M.: Iris recognition using histogram analysis. In: Proceedings of 38th Asilomar Conference on Signals, Systems, and Computers, pp. 562–566. IEEE, New York (2004). doi: 10.1109/ACSSC.2004.1399196

    Google Scholar 

  225. Jagadeesan, A., T.Thillaikkarasi, K.Duraiswamy: Cryptographic key generation from multiple biometric modalities: Fusing minutiae with iris feature. Int. J. Comput. Appl. 2(6), 16–26 (2010)

    Google Scholar 

  226. Jain, A., Uludag, U.: Hiding biometric data. IEEE Trans. Pattern Anal. Mach. Intell. 25(11), 1494–1498 (2003). doi: 10.1109/TPAMI.2003.1240122

    Google Scholar 

  227. Jain, A.K., Flynn, P., Ross, A.A.: Handbook of Biometrics. Springer, New York (2008)

    Google Scholar 

  228. Jain, A.K., Nandakumar, K., Nagar, A.: Biometric template security. EURASIP J. Adv. Signal Process. 2008, 1–17 (2008). doi: 10.1155/2008/579416

    Google Scholar 

  229. Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. IEEE Trans. Circ. Syst. Video Tech. 14, 4–20 (2004). doi: 10.1109/TCSVT.2003.818349

    Google Scholar 

  230. Jain, A.K., Ross, A., Uludag, U.: Biometric template security: Challenges and solutions. In: Proceedings of European Signal Processing Conference, pp. 1–4 (2005)

    Google Scholar 

  231. Jain, A.K., Uludag, U.: Hiding fingerprint minutiae in images. In: Proceedings of 3rd Workshop on Automatic Identification Advanced Technologies, pp. 97–102 (2002)

    Google Scholar 

  232. Jeffers, J., Arakala, A.: Minutiae-based structures for a fuzzy vault. In: Proceedings of Biometric Consortium Conference, pp. 1–6. IEEE, New York (2006). doi: 10.1109/BCC.2006.4341622

    Google Scholar 

  233. Jenisch, S., Lukesch, S., Uhl, A.: Comparison of compression algorithms’ impact on iris recognition accuracy II: revisiting JPEG. In: Delp, E., Wong, P., Dittmann, J., Memon, N. (eds.) Security, Forensics, Steganography, and Watermarking of Multimedia Contents X, Proceedings of SPIE, vol. 6819, pp. 68,190M.1–9. SPIE, Bellingham, WA (2008). doi: 10.1117/12.766360

    Google Scholar 

  234. Jenisch, S., Uhl, A.: Security analysis of a cancelable iris recognition system based on block remapping. In: Proceedings of IEEE International Conference on Image Processing, pp. 3274–3277. IEEE, New York (2011). doi: 10.1109/ICIP.2011.6115590

    Google Scholar 

  235. Jeong, D.S., Hwang, J.W., Kang, B.J., Park, K.R., Won, C.S., Park, D.K., Kim, J.: A new iris segmentation method for non-ideal iris images. Image Vis. Comput. 28(2), 254–260 (2010). doi: 10.1016/j.imavis.2009.04.001

    Google Scholar 

  236. Jeong, G.M., Kim, C., Ahn, H.S., Ahn, B.J.: JPEG quantization table design for face images and its application to face recognition. IEICE Trans. Fund. Electron. Comm. Comput. Sci. E69-A(11), 2990–2993 (2006)

    Google Scholar 

  237. Jeong, M.Y., Lee, C., Kim, J., Choi, J.Y., Toh, K.A., Kim, J.: Changeable biometrics for appearance based face recognition. In: Proceedings of Biometric Consortium Conference, pp. 1–5. IEEE, New York (2006). doi: 10.1109/BCC.2006.4341629

    Google Scholar 

  238. Jin, L., Yuan, X., Satoh, S., Li, J., Xia, L.: A hybrid classifier for precise and robust eye detection. In: Proceedings of 18th International Conference on Pattern Recognition, pp. 731–735. IEEE, New York (2006). doi: 10.1109/ICPR.2006.81

    Google Scholar 

  239. Juels, A., Sudan, M.: A fuzzy vault scheme. In: Proceedings of IEEE International Symposium on Information Theory, p. 408. IEEE, New York (2002). doi: 10.1109/ISIT.2002.1023680

    Google Scholar 

  240. Juels, A., Wattenberg, M.: A fuzzy commitment scheme. In: Proceedings of 6th ACM Conference on Computer and Communications Security, pp. 28–36. ACM, New York (1999). doi: 10.1145/319709.319714

    Google Scholar 

  241. Kalka, N., Bartlow, N., Cukic, B.: An automated method for predicting iris segmentation failures. In: Proceedings of IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–8. IEEE, New York (2009). doi: 10.1109/BTAS.2009.5339062

    Google Scholar 

  242. Kalka, N.D., Zuo, J., Schmid, N.A., Cukic, B.: Image quality assessment for iris biometric. In: Flynn, P.J., Pankanti, S. (eds.) Conference on Biometric Technology for Human Identification III, Proceedings of SPIE, vol. 6202, pp. 61020D.1–11. SPIE, Bellingham, WA (2006). doi: 10.1117/12.666448

    Google Scholar 

  243. Kanade, S., Camara, D., Petrovska-Delacrtaz, D., Dorizzi, B.: Application of biometrics to obtain high entropy cryptographic keys. World Acad. Sci. Eng. Tech. 52 (2009)

    Google Scholar 

  244. Kanade, S., Petrovska-Delacretaz, D., Dorizzi, B.: Cancelable iris biometrics and using error correcting codes to reduce variability in biometric data. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 120–127. IEEE, New York (2009). doi: 10.1109/CVPR.2009.5206646

    Google Scholar 

  245. Kanade, S., Petrovska-Delacretaz, D., Dorizzi, B.: Multi-biometrics based cryptographic key regeneration scheme. In: Proceedings of IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–7. IEEE, New York (2009). doi: 10.1109/BTAS.2009.5339034

    Google Scholar 

  246. Kang, B.J., Park, K.R.: A study on iris image restoration. In: Kanade, T., Jain, A., Ratha, N. (eds.) Proceedings of 5th International Conference on Audio- and Video-Based Biometric Person Authentication. LNCS, vol. 3546, pp. 31–40. Springer, New York (2005). doi: 10.1007/11527923_4

    Google Scholar 

  247. Karakoyunlu, D., Sunar, B.: Differential template attacks on puf enabled cryptographic devices. In: Proceedings of IEEE Workshop on Information Forensics and Security, pp. 1–6. IEEE, New York (2010). doi: 10.1109/WIFS.2010.5711445

    Google Scholar 

  248. Kekre, H.B., Thepade, S.D., Jain, J., Agrawal, N.: Iris recognition using texture features extracted from haarlet pyramid. Int. J. Comput. Appl. 11(12), 1–5 (2010). Found. Comp. Sci.

    Google Scholar 

  249. Kekre, H.B., Thepade, S.D., Jain, J., Agrawal, N.: Iris recognition using texture features extracted from walshlet pyramid. In: Prof. International Conference & Workshop on Emerging Trends in Technology, pp. 76–81. ACM, New York (2011). doi: 10.1145/1980022.1980038

    Google Scholar 

  250. Kelkboom, E.J.C., Breebaart, J., Buhan, I., Veldhuis, R.N.J.: Analytical template protection performance and maximum key size given a gaussian modeled biometric source. In: Biometric Technology for Human Identification VII, Proceedings of SPIE, vol. 7667, pp. 76670D.1–12. SPIE, Bellingham, WA (2010). doi: 10.1117/12.850240

    Google Scholar 

  251. Kelkboom, E.J.C., Molina, G.G., Breebaart, J., Veldhuis, R.N.J., Kevenaar, T.A.M., Jonker, W.: Binary biometrics: An analytic framework to estimate the performance curves under gaussian assumption. IEEE Trans. Syst. Man Cybern. A Syst. Humans 40(3), 555–571 (2010). doi: 10.1109/TSMCA.2010.2041657

    Google Scholar 

  252. Kelkboom, E.R.C., Breebaart, J., Kevenaar, T.A.M., Buhan, I., Veldhuis, R.N.J.: Preventing the decodability attack based cross-matching in a fuzzy commitment scheme. IEEE Trans. Inform. Forensics Secur. 6(1), 107–121 (2011). doi: 10.1109/TIFS.2010.2091637

    Google Scholar 

  253. Kennell, L.R., Ives, R.W., Gaunt, R.M.: Binary morphology and local statistics applied to iris segmentation for recognition. In: Proceedings of IEEE International Conference on Image Processing, pp. 293–296. IEEE, New York (2006). doi: 10.1109/ICIP.2006.313183

    Google Scholar 

  254. Khan, M., Xie, L., Zhang, J.: Robust hiding of fingerprint-biometric data into audio signals. In: Lee, S.W., Li, S. (eds.) Proceedings of 2nd International Conference on Biometrics. LNCS, vol. 4642, pp. 702–712. Springer, New York (2007). doi: 10.1007/978-3-540-74549-5_74

    Google Scholar 

  255. Khan, M.K., Zhang, J., Wang, X.: Chaotic hash-based fingerprint biometric remote user authentication scheme on mobile devices. Chaos Solitons Fractals 35(3), 519–524 (2008). doi: 10.1016/j.chaos.2006.05.061

    Google Scholar 

  256. Kholmatov, A., Yanikoglu, B.: Biometric cryptosystem using online signatures. In: Levi, A., Savas, E., Yenigün, H., Balcisoy, S., Saygin, Y. (eds.) Proceedings of 21st International Symposium on Computer and Information Sciences. LNCS, vol. 4263, pp. 981–990. Springer, New York (2006). doi: 10.1007/11902140_102

    Google Scholar 

  257. Kim, T., Chung, Y., Jung, S., Moon, D.: Secure remote fingerprint verification using dual watermarks. In: Safavi-Naini, R., Yung, M. (eds.) Proceedings of 1st International Conference on Digital Rights Management. Technologies, Issues, Challenges and Systems. LNCS, vol. 6855, pp. 217–227. Springer, New York (2005). doi: 10.1007/11787952_17

    Google Scholar 

  258. Kim, W.G., Lee, H.: Multimodal biometric image watermarking using two-stage integrity verification. Signal Process. 89(12), 2385–2399 (2009). doi: 10.1016/j.sigpro.2009.04.014

    MATH  Google Scholar 

  259. Kim, Y., Toh, K.: A method to enhance face biometric security. In: Proceedings of IEEE 1st International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–6. IEEE, New York (2007). doi: 10.1109/BTAS.2007.4401913

    Google Scholar 

  260. Kittler, J., Hatef, M., Duin, R.P., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998). doi: 10.1109/34.667881

    Google Scholar 

  261. Ko, J.G., Gil, Y.H., Yoo, J.H., Chung, K.I.: A novel and efficient feature extraction method for iris recognition. ETRI J. 29(3), 399–401 (2007)

    Google Scholar 

  262. Koch, E., Zhao, J.: Towards robust and hidden image copyright labeling. In: Proceedings of IEEE International Workshop on Nonlinear Signal and Image Processing, pp. 452–455 (1995)

    Google Scholar 

  263. Koh, J., Govindaraju, V., Chaudhary, V.: A robust iris localization method using an active contour model and hough transform. In: Proceedings of 20th International Conference on Pattern Recognition, pp. 2852–2856. IEEE, New York (2010). doi: 10.1109/ICPR.2010.699

    Google Scholar 

  264. Kollias, N.: The spectroscopy of human melanin pigmentation. In: Zeise, L., Chedekel, M.R., Fitzpatrick, T.B. (eds.) Melanin: Its Role in Human Photoprotection, pp. 31–38. Valdenmar Publishing, Overland Park, KS (1995)

    Google Scholar 

  265. Komninos, N., Dimitriou, T.: Protecting biometric templates with image watermarking techniques. In: Lee, S.W., Li, S. (eds.) Proceedings of 2nd International Conference on Biometrics. LNCS, vol. 4642, pp. 114–123. Springer, New York (2007). doi: 10.1007/978-3-540-74549-5_13

    Google Scholar 

  266. Kong, A., Cheunga, K.H., Zhanga, D., Kamelb, M., Youa, J.: An analysis of BioHashing and its variants. Pattern Recogn. 39(7), 1359–1368 (2006). doi: 10.1016/j.patcog.2005.10.025

    MATH  Google Scholar 

  267. Kong, W., Zhang, D.: Accurate iris segmentation based on novel reflection and eyelash detection model. In: Proceedings of of 2001 International Symposium on Intelligent Multimedia, Video and Speech Processing, 2001. pp. 263–266 (2001). doi: 10.1109/ISIMP.2001.925384

    Google Scholar 

  268. Konrad, M., Stögner, H., Uhl, A.: Custom design of jpeg quantization tables for compressing iris polar images to improve recognition accuracy. In: Tistarelli, M., Nixon, M. (eds.) Proceedings of 3rd International Conference on Biometrics. LNCS, vol. 5558, pp. 1091–1101. Springer, New York (2009). doi: 10.1007/978-3-642-01793-3_110

    Google Scholar 

  269. Konrad, M., Stögner, H., Uhl, A.: Evolutionary optimization of JPEG quantization tables for compressing iris polar images in iris recognition systems. In: Proceedings of 6th International Symposium on Image and Signal Processing and Analysis, pp. 534–539. IEEE, New York (2009)

    Google Scholar 

  270. Konrad, M., Stögner, H., Uhl, A., Wild, P.: Computationally efficient serial combination of rotation-invariant and rotation compensating iris recognition algorithms. In: Proceedings of 5th International Conference on Computer Vision Theory and Applications, vol. 1, pp. 85–90. SciTePress, Lynnfield, MA (2010)

    Google Scholar 

  271. Kostmajer, G., Stögner, H., Uhl, A.: Custom jpeg quantization for improved iris recognition accuracy. In: Gritzalis, D., Lopez, J. (eds.) Proceedings of 24th IFIP International Information Security Conference, IFIP, vol. 297, pp. 78–86. Springer, New York (2009). doi: 10.1007/978-3-642-01244-0_7

    Google Scholar 

  272. Kovesi, P.: Image features from phase congruency. Videre J. Comput. Vis. Res. 1(3), 1–26 (1999)

    Google Scholar 

  273. Krichen, E., Dorizzi, B., Sun, Z., Garcia-Salicetti, S.: Iris recognition. In: Petrovska-Delacretaz, D., Dorizzi, B., Chollet, G. (eds.) Guide to Biometric Reference Systems and Performance Evaluation, pp. 25–49. Springer, New York (2009). doi: 10.1007/978-1-84800-292-0_3

    Google Scholar 

  274. Krichen, E., Garcia-Salicetti, S., Dorizzi, B.: A new probabilistic iris quality measure for comprehensive noise detection. In: Proceedings of IEEE 1st International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–6. IEEE, New York (2007). doi: 10.1109/BTAS.2007.4401906

    Google Scholar 

  275. Krichen, E., Mellakh, A., Anh, P.V., Salicetti, S., Dorizzi, B.: A biometric reference system for iris. OSIRIS version 2.01 (2009). URL http://svnext.it-sudparis.eu/svnview2-eph/ref\_syst/Iris\_Osiris/

  276. Kronfeld, P.: Gross anatomy and embryology of the eye. In: Davison, H. (ed.) The Eye. Academic, London (1962)

    Google Scholar 

  277. Kroon, B., Hanjalic, A., Maas, S.: Eye localization for face matching: is it always useful and under what conditions? In: Proceedings of International Conference on Content-based Image and Video Retrieval, pp. 379–388. ACM, New York (2008). doi: 10.1145/1386352.1386401

    Google Scholar 

  278. Kuan, Y.W., Teoh, A.B.J., Ngo, D.C.L.: Secure hashing of dynamic hand signatures using wavelet-fourier compression with biophasor mixing and discretization. EURASIP J. Appl. Signal Process. 2007(1), 32–32 (2007). doi: 10.1155/2007/59125

    Google Scholar 

  279. Kumar, A., Kumar, A.: A palmprint based cryptosystem using double encryption. In: Kumar, B.V., Prabhakar, S., Ross, A. (eds.) Conference on Biometric Technology for Human Identification V, Proceedings of SPIE, vol. 6944, pp. 69,440D.1–9. SPIE, Bellingham, WA (2008). doi: 10.1117/12.778833

    Google Scholar 

  280. Kumar, A., Kumar, A.: Development of a new cryptographic construct using palmprint-based fuzzy vault. EURASIP J. Adv. Signal Process. 2009, 967,046.1–11 (2009). doi: 10.1155/2009/967046

    Google Scholar 

  281. Kümmel, K., Vielhauer, C.: Reverse-engineer methods on a biometric hash algorithm for dynamic handwriting. In: Proceedings of 12th Workshop on Multimedia and Security, pp. 67–72. ACM, New York (2010). doi: 10.1145/1854229.1854244

    Google Scholar 

  282. Kundur, D.: Watermarking with diversity: Insights and implications. IEEE Multimed. J. 8(4), 46–52 (2001). doi: 10.1109/93.959102

    Google Scholar 

  283. Kundur, D., Hatzinakos, D.: Digital watermarking using multiresolution wavelet decomposition. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 5, pp. 2969–2972. IEEE, New York (1998). doi: 10.1109/ICASSP.1998.678149

    Google Scholar 

  284. Kutter, M., Jordan, F., Bossen, F.: Digital signature of color images using amplitude modulation. In: Sethi, I.K., Jain, R.C. (eds.) Storage and Retrieval for Image and Video Databases V, Proceedings of SPIE, vol. 3022, pp. 518–526. SPIE, Bellingham, WA (1997). doi: 10.1117/12.263442

    Google Scholar 

  285. L-1 Identity Solutions: MobileEyes. URL http://www.l1id.com/files/708-2011-04-04\_Mobile-Eyes\_data-sheet.pdf. Retrieved May 2012

  286. L-1 Identity Solutions: HIIDE Series 4. URL http://www.l1id.com/files/224-HIIDE\_0908\_final.pdf. Retrieved May 2012

  287. L-1 Identity Solutions: Pier 2.4. URL http://www.aditech.co.uk/downloads/PIER24.pdf. Retrieved May 2012

  288. L-1 Identity Solutions: Pier-T. URL http://www.l1id.com/files/205-PIER-T\_0908\_final.pdf. Retrieved May 2012

  289. Labati, R.D., Scotti, F.: Noisy iris segmentation with boundary regularization and reflections removal. Image Vis. Comput. 28(2), 270–277 (2010). doi: 10.1016/j.imavis.2009.05.004

    Google Scholar 

  290. Labati, R.D., Piuri, V., Scotti, F.: Agent-based image iris segmentation and multipleviews boundary refining. In: Proceedings of IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, pp. 204–210. IEEE, New York (2009). doi: 10.1109/BTAS.2009.5339077

    Google Scholar 

  291. Lalithamani, N., Soman, K.: An effective scheme for generating irrevocable cryptographic key from cancelable fingerprint templates. Int. J. Comput. Sci. Netw. Secur. 9(3), 183–193 (2009)

    Google Scholar 

  292. Lalithamani, N., Soman, K.: Irrevocable cryptographic key generation from cancelable fingerprint templates: An enhanced and effective scheme. Eur. J. Sci. Res. 31, 372–387 (2009)

    Google Scholar 

  293. Lalithamani, N., Soman, K.P.: Towards generating irrevocable key for cryptography from cancelable fingerprints. In: Proceedings of International Conference on Computer Science and Information Technology, pp. 563–568. IEEE, New York (2009). doi: 10.1109/ICCSIT.2009.5234801

    Google Scholar 

  294. Lang, A., Dittmann, J.: Digital watermarking of biometric speech references: impact to the eer system performance. In: Delp, E., Wong, P. (eds.) Security, Steganography, and Watermarking of Multimedia Contents IX, Proceedings of SPIE, vol. 6505, pp. 650513.1–12. SPIE, Bellingham, WA (2007). doi: 10.1117/12.703890

    Google Scholar 

  295. Lee, C., Choi, J., Toh, K., Lee, S., Kim, J.: Alignment-free cancelable fingerprint templates based on local minutiae information. IEEE Trans. Syst. Man Cybern. B Cybern. 37(4), 980–992 (2007). doi: 10.1109/TSMCB.2007.896999

    Google Scholar 

  296. Lee, H., Lim, J., Yu, S., Kim, S., Lee, S.: Biometric image authentication using watermarking. In: Proceedings of International Joint Conference SICE-ICASE, pp. 3950–3953. IEEE, New York (2006). doi: 10.1109/SICE.2006.314934

    Google Scholar 

  297. Lee, Y., Micheals, R.J., Filliben, J.J., Phillips, P.J.: Robust Iris Recognition Baseline for the Grand Challenge. Nistir report 7777, NIST (2011)

    Google Scholar 

  298. Lee, Y., Phillips, P.J., Micheals, R.J.: An automated video-based system for iris recognition. In: Tistarelli, M., Nixon, M. (eds.) Proceedings of 3rd International Conference on Biometrics. LNCS, vol. 5558, pp. 1160–1169. Springer, New York (2009). doi: 10.1007/978-3-642-01793-3_117

    Google Scholar 

  299. Lee, Y.J., Bae, K., Lee, S.J., Park, K.R., Kim, J.: Biometric key binding: Fuzzy vault based on iris images. In: Lee, S.W., Li, S. (eds.) Proceedings of 2nd International Conference on Biometrics, LNCS, vol. 4642, pp. 800–808. Springer, New York (2007). doi: 10.1007/978-3-540-74549-5_84

    Google Scholar 

  300. Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions, and reversals. Sov. Phys. Dokl. 10(8), 707–710 (1966)

    MathSciNet  Google Scholar 

  301. LG Iris: Iris Access 2200. URL http://www.lgiris.com/ps/products/previousmodels/irisaccess2200.htm. Retrieved May 2012

  302. LG Iris: Iris Access EOU3000. URL http://www.lgiris.com/ps/products/previousmodels/ia3k/eou3000.htm. Retrieved May 2012

  303. Li, C., Ma, B., Wang, Y., Zhang, Z.: Protecting biometric templates using authentication watermarking. In: Qiu, G., Lam, K., Kiya, H., Xue, X.Y., Kuo, C.C., Lew, M. (eds.) Proceedings of 11th Pacific Rim Conference on Multimedia. LNCS, vol. 6297, pp. 709–718. Springer, New York (2010). doi: 10.1007/978-3-642-15702-8_65

    Google Scholar 

  304. Li, H., Wang, M., Pang, L., Zhang, W.: Key binding based on biometric shielding functions. In: Proceedings of International Conference on Information Assurance and Security, pp. 19–22. IEEE, New York (2009). doi: 10.1109/IAS.2009.305

    Google Scholar 

  305. Li, P., Liu, X., Xiao, L., Song, Q.: Robust and accurate iris segmentation in very noisy iris images. Image Vis. Comput. 28(2), 246–253 (2010). doi: 10.1016/j.imavis.2009.04.010

    Google Scholar 

  306. Li, P., Ma, H.: Iris recognition in non-ideal imaging conditions. Pattern Recogn. Lett. 33(8), 1012–1018 (2012). doi: 10.1016/j.patrec.2011.06.017

    Google Scholar 

  307. Li, P., Yang, X., Cao, K., Tao, X., Wang, R., Tian, J.: An alignment-free fingerprint cryptosystem based on fuzzy vault scheme. J. Netw. Comput. Appl. 33 (2010). doi: 10.1016/j.jnca.2009.12.003

    Google Scholar 

  308. Li, Q., Chang, E.C.: Hiding secret points amidst chaff. In: Vaudenay, S. (ed.) Proceedings of International Conference on Theory And Applications of Cryptographic Techniques. LNCS, vol. 4004, pp. 59–72. Springer, New York (2006). doi: 10.1007/11761679_5

    Google Scholar 

  309. Li, Q., Chang, E.C.: Robust, short and sensitive authentication tags using secure sketch. In: Proceedings of 8th Workshop on Multimedia and Security, pp. 56–61. ACM, New York (2006). doi: 10.1145/1161366.1161377

    Google Scholar 

  310. Li, Q., Guo, M., Chang, E.C.: Fuzzy extractors for asymmetric biometric representations. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshop, pp. 1–6. IEEE, New York (2008). doi: 10.1109/CVPRW.2008.4563113

    Google Scholar 

  311. Li, Q., Sutcu, Y., Memon, N.: Secure sketch for biometric templates. In: Lai, X., Chen, K. (eds.) Proceedings of 12th International Conference on Theory and Applications of Cryptology and Information Security. LNCS, vol. 4284, pp. 99–113. Springer, New York (2006). doi: 10.1007/11935230_7

    Google Scholar 

  312. Li, S.: Encyclopedia of Biometrics. Springer, New York (2009)

    Google Scholar 

  313. Liam, L.W., Chekima, A., Fan, L.C., Dargham, J.: Iris recognition using self-organizing neural network. In: Proceedings of Student Conference on Research and Development, pp. 169–172. IEEE, New York (2002). doi: 10.1109/SCORED.2002.1033084

    Google Scholar 

  314. Lienhart, R., Kuranov, A., Pisarevsky, V.: Empirical analysis of detection cascades of boosted classifiers for rapid object detection. In: Michaelis, B., Krell, G. (eds.) Proceedings of 25th DAGM Symposium, LNCS, vol. 2781, pp. 297–304. Springer, New York (2003). doi: 10.1007/978-3-540-45243-0_39

    Google Scholar 

  315. Linnartz, J.P., Tuyls, P.: New shielding functions to enhance privacy and prevent misuse of biometric templates. In: Kittler, J., Nixon, M. (eds.) Proceedings of 4th International Conference on Audio- and Video-Based Biometric Person Authentication. LNCS, vol. 2688, pp. 393–402. Springer, New York (2003). doi: 10.1007/3-540-44887-X_47

    Google Scholar 

  316. Liu, X., Bowyer, K., Flynn, P.: Experiments with an improved iris segmentation algorithm. In: Proceedings of 4th IEEE Workshop on Automatic Identification Advanced Technologies, pp. 118–123. IEEE, New York (2005). doi: 10.1109/AUTOID.2005.21

    Google Scholar 

  317. Lodin, A., Kovacs, L., Demea, S.: Interface of an iris detection program. In: Proceedings of 30th International Spring Seminar on Electronics Technology, pp. 555–558. IEEE, New York (2007). doi: 10.1109/ISSE.2007.4432918

    Google Scholar 

  318. Low, C.Y., Teoh, A.B.J., Tee, C.: Fusion of LSB and DWT Biometric Watermarking Using Offline Handwritten Signature for Copyright Protection. In: Tistarelli, M., Nixon, M. (eds.) Proceedings of 3rd International Conference on Biometrics. LNCS, vol. 5558, pp. 786–795. Springer, New York (2009). doi: 10.1007/978-3-642-01793-3_80

    Google Scholar 

  319. Lu, H., Martin, K., Bui, F., Plataniotis, K., Hatzinakos, D.: Face recognition with biometric encryption for privacy-enhancing self-exclusion. In: Proceedings of 16th International Conference on Digital Signal Processing, pp. 1–8. IEEE, New York (2009). doi: 10.1109/ICDSP.2009.5201257

    Google Scholar 

  320. Luengo-Oroz, M.A., Faure, E., Angulo, J.: Robust iris segmentation on uncalibrated noisy images using mathematical morphology. Image Vis. Comput. 28, 278–284 (2010). doi: 10.1016/j.imavis.2009.04.018

    Google Scholar 

  321. Lumini, A., Nanni, L.: An improved BioHashing for human authentication. Pattern Recogn. 40(3), 1057–1065 (2007). doi: 10.1016/j.patcog.2006.05.030

    MATH  Google Scholar 

  322. Ma, L., Tan, T., Wang, Y., Zhang, D.: Personal identification based on iris texture analysis. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1519–1533 (2003). doi: 10.1109/TPAMI.2003.1251145

    Google Scholar 

  323. Ma, L., Tan, T., Wang, Y., Zhang, D.: Efficient iris recognition by characterizing key local variations. IEEE Trans. Image Process. 13(6), 739–750 (2004). doi: 10.1109/ TIP.2004.827237

    Google Scholar 

  324. Maiorana, E.: Biometric cryptosystem using function based on-line signature recognition. Expert Syst. Appl. 37(4), 3454–3461 (2010). doi: 10.1016/j.eswa.2009.10.043

    Google Scholar 

  325. Maiorana, E., Campisi, P., Fierrez, J., Ortega-Garcia, J., Neri, A.: Cancelable templates for sequence-based biometrics with application to on-line signature recognition. IEEE Trans. Syst. Man Cybern. A Syst. Humans 40(3), 525–538 (2010). doi: 10.1109/TSMCA.2010.2041653

    Google Scholar 

  326. Maiorana, E., Campisi, P., Neri, A.: Biometric signature authentication using radon transform-based watermarking techniques. In: Proceedings of Biometrics Symposium, pp. 1–6. IEEE, New York (2007). doi: 10.1109/BCC.2007.4430543

    Google Scholar 

  327. Maiorana, E., Campisi, P., Neri, A.: User adaptive fuzzy commitment for signature templates protection and renewability. J. Electron. Imag. 17(1), 011,011.1–12 (2008). doi: 10.1117/1.2885239

    Google Scholar 

  328. Maiorana, E., Ercole, C.: Secure Biometric Authentication System Architecture using Error Correcting Codes and Distributed Cryptography. In: Proceedings of Gruppo nazionale Telecomunicazioni e Teoria dell’Informazione, pp. 1–12 (2007)

    Google Scholar 

  329. Maiorana, E., Martinez-Diaz, M., Campisi, P., Ortega-Garcia, J., Neri, A.: Cancelable biometrics for hmm-based signature recognition. In: Proceedings of IEEE 2nd International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–6. IEEE, New York (2008). doi: 10.1109/BTAS.2008.4699360

    Google Scholar 

  330. Maiorana, E., Martinez-Diaz, M., Campisi, P., Ortega-Garcia, J., Neri, A.: Template protection for hmm-based on-line signature authentication. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshop, pp. 1–6. IEEE, New York (2008). doi: 10.1109/CVPRW.2008.4563114

    Google Scholar 

  331. Malladi, R., Sethian, J., Vemuri, B.: Shape modeling with front propagation: a level set approach. IEEE Trans. Pattern Anal. Mach. Intell. 17(2), 158–175 (1995). doi: 10.1109/34.368173

    Google Scholar 

  332. Maltoni, D., Maio, D., Jain, A., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer, New York (2005)

    Google Scholar 

  333. Mao, Y., Wu, M.: Security evaluation for communication-friendly encryption of multimedia. In: Proceedings of IEEE International Conference on Image Processing, pp. 569–572. IEEE, New York (2004). doi: 10.1109/ICIP.2004.1418818

    Google Scholar 

  334. de Martin-Roche, D., Sanchez-Avila, C., Sanchez-Reillo, R.: Iris recognition for biometric identification using dyadic wavelet transform zero-crossing. In: Proceedings of 35th International Carnahan Conference on Security Technology, pp. 272–277. IEEE, New York (2001). doi: 10.1109/.2001.962844

    Google Scholar 

  335. Masek, L.: Recognition of human iris patterns for biometric identification. Master’s thesis, University of Western Australia (2003)

    Google Scholar 

  336. Masek, L., Kovesi, P.: MATLAB Source Code for a Biometric Identification System Based on Iris Patterns (2003). URL http://www.csse.uwa.edu.au/~pk/studentprojects/libor/sourcecode.html. Retrieved May 2012

  337. Matey, J., Naroditsky, O., Hanna, K., Kolczynski, R., LoIacono, D., Mangru, S., Tinker, M., Zappia, T., Zhao, W.Y.: Iris on the move: Acquisition of images for iris recognition in less constrained environments. Proc. IEEE 94, 1936–1947 (2006). doi: 10.1109/JPROC.2006.884091

    Google Scholar 

  338. Matey, J.R., Broussard, R., Kennell, L.: Iris image segmentation and sub-optimal images. Image Vis. Comput. 28(2), 215–222 (2010). doi: 10.1016/j.imavis.2009.05.006

    Google Scholar 

  339. Matschitsch, S., Stögner, H., Tschinder, M., Uhl, A.: Rotation-invariant iris recognition: boosting 1D spatial-domain signatures to 2D. In: Filipe, J., Cetto, J., Ferrier, J.L. (eds.) Proceedings of 5th International Conference on Informatics in Control, Automation and Robotics, pp. 232–235. SciTePress (2008)

    Google Scholar 

  340. Matschitsch, S., Tschinder, M., Uhl, A.: Comparison of compression algorithms’ impact on iris recognition accuracy. In: Lee, S.W., Li, S. (eds.) Proceedings of 2nd International Conference on Biometrics, LNCS, vol. 4642, pp. 232–241. Springer, New York (2007). doi: 10.1007/978-3-540-74549-5_25

    Google Scholar 

  341. Meenakshi, V.S., Padmavathi, G.: Security analysis of password hardened multimodal biometric fuzzy vault. World Acad. Sci. Eng. Tech. 56, 312–320 (2009)

    Google Scholar 

  342. Meerwald, P.: Watermarking Toolbox. URL http://www.cosy.sbg.ac.at/~pmeerw/Watermarking/source/. Retrieved June 2012

  343. Meerwald, P., Uhl, A.: A survey of wavelet-domain watermarking algorithms. In: Wong, P.W., Delp, E.J. (eds.) Electronic Imaging, Security and Watermarking of Multimedia Contents III, Proceedings of SPIE, vol. 4314, pp. 505–516. SPIE, Bellingham, WA (2001). doi: 10.1117/12.435434

    Google Scholar 

  344. Mehrotra, H., Rattani, A., Gupta, P.: Fusion of iris and fingerprint biometric for recognition. In: Proceedings of International Conference on Signal and Image Processing, pp. 1–6. ACTA Press, Calgary, AB (2006)

    Google Scholar 

  345. Micilotta, A., Jon, E., Bowden, O.: Detection and tracking of humans by probabilistic body part assembly. In: Proceedings of British Machine Vision Conference, pp. 429–438. BMVA Press, Guildford (2005)

    Google Scholar 

  346. Mihailescu, P.: The fuzzy vault for fingerprints is vulnerable to brute force attack. Report arxiv:0708.2974v1, Cornell University (2007)

    Google Scholar 

  347. Miyazawa, K., Ito, K., Aoki, T., Kobayashi, K., Nakajima, H.: A phase-based iris recognition algorithm. In: Zhang, D., Jain, A. (eds.) Proceedings of 1st International Conference on Biometrics, LNCS, vol. 3832, pp. 356–365. Springer, New York (2006). doi: 10.1007/11608288_48

    Google Scholar 

  348. Monro, D.M., Rakshit, S., Zhang, D.: Dct-based iris recognition. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 586–595 (2007). doi: 10.1109/TPAMI.2007.1002

    Google Scholar 

  349. Monrose, F., Reiter, M.K., Li, Q., Wetzel, S.: Cryptographic Key Generation from Voice. In: Proceedings of IEEE Symposium on Security and Privacy, pp. 202–213. IEEE, New York (2001). doi: 10.1109/SECPRI.2001.924299

    Google Scholar 

  350. Monrose, F., Reiter, M.K., Li, Q., Wetzel, S.: Using Voice to Generate Cryptographic Keys. In: Proceedings of Speaker Odyssey 2001, The Speech Recognition Workshop, pp. 237–242 (2001)

    Google Scholar 

  351. Monrose, F., Reiter, M.K., Lopresti, D.P., Shih, C.: Toward speech-generated cryptographic keys on resource constrained devices. In: Proceedings of 11th USENIX Security Symposium, pp. 283–296 (2002)

    Google Scholar 

  352. Monrose, F., Reiter, M.K., Wetzel, S.: Password hardening based on keystroke dynamics. In: Proceedings of 6th ACM Conference on Computer and Communications Security, pp. 73–82. ACM, New York (1999). doi: 10.1145/319709.319720

    Google Scholar 

  353. Moon, D., Choi, W.Y., Moon, K., Chung, Y.: Fuzzy fingerprint vault using multiple polynomials. In: Proceedings of IEEE 13th International Symposium on Consumer Electronics, pp. 290–293. IEEE, New York (2009). doi: 10.1109/ISCE.2009.5156914

    Google Scholar 

  354. Moon, D., Kim, T., Jung, S.H., Chung, Y., Moon, K., Ahn, D., Kim, S.: Performance evaluation of watermarking techniques for secure multimodal biometric systems. In: Hao, Y., Liu, J., Wang, Y.P., ming Cheung, Y., Yin, H., Jiao, L., Yong-Chang Jiao, J.M. (eds.) Proceedings of International Conference on Computational Intelligence and Security. LNCS, vol. 3802, pp. 635–642. Springer, New York (2005). doi: 10.1007/11596981_94

    Google Scholar 

  355. Morimoto, C.H., Santos, T.T., Muniz, A.S.: Automatic iris segmentation using active near infra red lighting. In: Proceedings of 18th Brazilian Symposium on Computer Graphics and Image Processing, pp. 37–43. IEEE, New York (2005). doi: 10.1109/SIBGRAPI.2005.14

    Google Scholar 

  356. Motwani, R.C., Harris, F.C., Bekris, K.E.: A proposed digital rights management system for 3d graphics using biometric watermarks. In: Proceedings of 7th IEEE Consumer Communications and Networking Conference, pp. 1075–1080. IEEE, New York (2010). doi: 10.1109/CCNC.2010.5421663

    Google Scholar 

  357. Multimedia University: MMU1 and MMU2 Iris Databases. URL http://pesona.mmu.edu.my/~ccteo/. Retrieved May 2012

  358. Mumford, D., Shah, J.: Optimal approximations by piecewise smooth functions and associated variational problems. Comm. Pure Appl. Math. 42(5), 577–685 (1989). doi: 10.1002/cpa.3160420503

    MathSciNet  MATH  Google Scholar 

  359. Myers, C.S., Rabiner, L.R.: A comparative study of several dynamic time-warping algorithms for connected word recognition. Bell Syst. Tech. J. 60(7), 1389–1409 (1981)

    Google Scholar 

  360. Nagar, A., Chaudhury, S.: Biometrics based Asymmetric Cryptosystem Design Using Modified Fuzzy Vault Scheme. In: Proceedings of 18th International Conference on Pattern Recognition, pp. 537–540. IEEE, New York (2006). doi: 10.1109/ICPR.2006.330

    Google Scholar 

  361. Nagar, A., Nandakumar, K., Jain, A.: A hybrid biometric cryptosystem for securing fingerprint minutiae templates. Pattern Recogn. Lett. 31, 733–741 (2010). doi: 10.1016/j.patrec.2009.07.003

    Google Scholar 

  362. Nagar, A., Nandakumar, K., Jain, A.K.: Securing fingerprint template: Fuzzy vault with minutiae descriptors. In: Proceedings of 19th International Conference on Pattern Recognition, pp. 1–4. IEEE, New York (2008). doi: 10.1109/ICPR.2008.4761459

    Google Scholar 

  363. Nandakumar, K.: A fingerprint cryptosystem based on minutiae phase spectrum. In: Proceedings of IEEE Workshop on Information Forensics and Security, pp. 1–6. IEEE, New York (2010). doi: 10.1109/WIFS.2010.5711456

    Google Scholar 

  364. Nandakumar, K., Jain, A.K.: Multibiometric template security using fuzzy vault. In: Proceedings of IEEE 2nd International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–6. IEEE, New York (2008). doi: 10.1109/BTAS.2008.4699352

    Google Scholar 

  365. Nandakumar, K., Jain, A.K., Pankanti, S.: Fingerprint-based Fuzzy Vault: Implementation and Performance. IEEE Trans. Inform. Forensics Secur. 2, 744–757 (2007). doi: 10.1109/TIFS.2007.908165

    Google Scholar 

  366. Nanni, L., Lumini, A.: Random subspace for an improved biohashing for face authentication. Pattern Recogn. Lett. 29(3), 295–300 (2008). doi: 10.1016/j.patrec.2007.10.005

    Google Scholar 

  367. Nanni, L., Lumini, A.: A combination of face/eye detectors for a high performance face detection system. IEEE Multimed. PP(99), 1–15 (2011). doi: 10.1109/MMUL.2011.57

    Google Scholar 

  368. Newman, C.: A life revealed. Natl. Geogr. 2002 (2002)

    Google Scholar 

  369. Nguyen, K., Fookes, C., Sridharan, S.: Fusing shrinking and expanding active contour models for robust iris segementation. In: Proceedings of 10th International Conference on Information Sciences Signal Processing and their Applications, pp. 185–188. IEEE, New York (2010). doi: 10.1109/ISSPA.2010.5605546

    Google Scholar 

  370. Nishino, K., Nayar, S.K.: Eyes for relighting. ACM Trans. Graph. 23, 704–711 (2004). doi: 10.1145/1015706.1015783

    Google Scholar 

  371. NIST: Face Recognition Vendor Test 2006. URL http://www.nist.gov/itl/iad/ig/frvt-2006.cfm. Retrieved May 2012

  372. NIST: Iris Challenge Evaluation. URL http://iris.nist.gov/ice. Retrieved May 2012

  373. NIST: IRIS Exchange Program. URL http://iris.nist.gov/irex/. Retrieved May 2012

  374. NIST: Multiple Biometric Grand Challenge. URL http://www.nist.gov/itl/iad/ig/mbgc.cfm. Retrieved May 2012

  375. NIST: VASIR Source Code Beta V1.0. URL http://www.nist.gov/itl/iad/ig/vasir.cfm. Retrieved May 2012

  376. Noh, S.I., Bae, K., Park, Y., Kim, J.: A novel method to extract features for iris recognition system. In: Kittler, J., Nixon, M. (eds.) Proceedings of 4th International Conference on Audio- and Video-Based Biometric Person Authentication. LNCS, vol. 2688, pp. 862–868. Springer, New York (2003). doi: 10.1007/3-540-44887-X_100

    Google Scholar 

  377. Noore, A., Singh, R., Vatsa, M., Houck, M.: Enhancing security of fingerprints through contextual biometric watermarking. Forensic Sci. Int. 169, 188–194 (2007)

    Google Scholar 

  378. Nuance, USA: URL http://www.nuance.com/. Retrieved June 2012

  379. OKI: IrisPass-H. URL http://www.pro-4-pro.com/media/product\_data/9354428\_1\_p4p0804/download.pdf. Retrieved May 2012

  380. OKI: IrisPass-M. URL http://www.oki.com/en/otr/2006/n205/pdf/otr-205-R06.pdf. Retrieved May 2012

  381. Ouda, O., Tsumura, N., Nakaguchi, T.: Bioencoding: A reliable tokenless cancelable biometrics scheme for protecting iriscodes. IEICE Trans. Inform. Syst. E93.D, 1878–1888 (2010). doi: 10.1587/transinf.E93.D.1878

    Google Scholar 

  382. Ouda, O., Tsumura, N., Nakaguchi, T.: Tokenless cancelable biometrics scheme for protecting iris codes. In: Proceedings of 20th International Conference on Pattern Recognition, pp. 882–885. IEEE, New York (2010). doi: 10.1109/ICPR.2010.222

    Google Scholar 

  383. Palacky University of Olomouc: Iris Database. URL http://phoenix.inf.upol.cz/iris/. Retrieved May 2012

  384. Panasonic: BM-ET200. URL http://www.aditech.co.uk/downloads/PanasonicBM-ET200.pdf. Retrieved May 2012

  385. Panasonic: BM-ET330. URL ftp://ftp.panasonic.com/pub/Panasonic/cctv/SpecSheets/BM-ET330.pdf. Retrieved May 2012

  386. Park, C.H., Lee, J.J.: Extracting and combining multimodal directional iris features. In: Zhang, D., Jain, A. (eds.) Proceedings of 1st International Conference on Biometrics, LNCS, vol. 3832, pp. 389–396. Springer, New York (2006). doi: 10.1007/11608288_52

    Google Scholar 

  387. Park, K.R., Jeong, D.S., Kang, B.J., Lee, E.C.: A study on iris feature watermarking on face data. In: Beliczynski, B., Dzielinski, A., Ribeiro, B. (eds.) Proceedings of 8th International Conference on Adaptive and Natural Computing Algorithms. LNCS, vol. 4432, pp. 415–423. Springer, New York (2007). doi: 10.1007/978-3-540-71629-7_47

    Google Scholar 

  388. Pavlov, I.: 7ZIP. URL http://www.7-zip.org/. Retrieved June 2012

  389. Pennebaker, W., Mitchell, J.: JPEG – Still image compression standard. Van Nostrand Reinhold (1993)

    Google Scholar 

  390. Perona, P., Malik, J.: Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12(7), 629–639 (1990). doi: 10.1109/34.56205

    Google Scholar 

  391. Phillips, P., Bowyer, K., Flynn, P., Liu, X., Scruggs, W.: The iris challenge evaluation 2005. In: Proceedings of IEEE 2nd International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–8. IEEE, New York (2008). doi: 10.1109/BTAS.2008.4699333

    Google Scholar 

  392. Phillips, P.J., Flynn, P.J., Beveridge, J.R., Scruggs, W.T., O’Toole, A.J., Bolme, D., Bowyer, K.W., Draper, B.A., Givens, G.H., Lui, Y.M., Sahibzada, H., Scallan Iii, J.A., Weimer, S.: Overview of the multiple biometrics grand challenge. In: Tistarelli, M., Nixon, M. (eds.) Proceedings of 3rd International Conference on Biometrics. LNCS, vol. 5558, pp. 705–714. Springer, New York (2009). doi: 10.1007/978-3-642-01793-3_72

    Google Scholar 

  393. Phillips, P.J., Member, S., Bowyer, K.W., Flynn, P.J.: Comments on the casia version 1.0 iris dataset. IEEE Trans. Pattern Anal. Mach. Intell. 29(10), 1869–1870 (2007). doi: 10.1109/TPAMI.2007.1137

    Google Scholar 

  394. Phillips, P.J., Scruggs, W.T., O’toole, A.J., Flynn, P.J., Kevin, W., Schott, C.L., Sharpe, M.: FRVT 2006 and ICE 2006 Large-Scale Results. NISTIR 7408 Report, NIST (2007)

    Google Scholar 

  395. Pillai, J.K., Patel, V.M., Chellappa, R., Ratha, N.K.: Sectored random projections for cancelable iris biometrics. In: Proceedings of IEEE International Conference on Acoustics Speech and Signal Processing, pp. 1838–1841. IEEE, New York (2010). doi: 10.1109/ICASSP.2010.5495383

    Google Scholar 

  396. Pla, O.G., Lin, E.T., Delp, E.J.: A wavelet watermarking algorithm based on a tree structure. In: Delp, E.J., Wong, P.W. (eds.) Security, Steganography, and Watermarking of Multimedia Contents VI, Proceedings of SPIE, vol. 5306, pp. 571–580. SPIE, Bellingham, WA (2004). doi: 10.1117/12.531459

    Google Scholar 

  397. Poon, H.T., Miri, A.: A collusion attack on the fuzzy vault scheme. ISC Int. J. Inform. Secur. 1(1), 27–34 (2009)

    Google Scholar 

  398. Poursaberi, A., Araabi, B.N.: Iris recognition for partially occluded images: methodology and sensitivity analysis. EURASIP J. Appl. Signal Process. 2007, 36751.1–12 (2007). doi: 10.1155/2007/36751

    Google Scholar 

  399. Prasanalakshmi, B., Kannammal, A.: A secure cryptosystem from palm vein biometrics. In: Proceedings of 2nd International Conference on Interaction Sciences, pp. 1401–1405. ACM, New York (2009). doi: 10.1145/1655925.1656183

    Google Scholar 

  400. Precise Biometrics AB, Sweden: URL http://www.precisebiometrics.com/. Retrieved June 2012

  401. priv-ID B.V., The Netherlands: URL http://www.priv-id.com/. Retrieved June 2012

  402. Proença, H.: Iris recognition: A method to segment visible wavelength iris images acquired on-the-move and at-a-distance. In: Bebis, G., Boyle, R.D., Parvin, B., Koracin, D., Remagnino, P., Porikli, F.M., Peters, J., Klosowski, J.T., Arns, L.L., Chun, Y.K., Rhyne, T.M., Monroe, L. (eds.) Proceedings of International Symposium on Visual Computing. LNCS, vol. 5358, pp. 731–742. Springer, New York (2008). doi: 10.1007/978-3-540-89639-5_70

    Google Scholar 

  403. Proença, H.: Iris recognition: On the segmentation of degraded images acquired in the visible wavelength. IEEE Trans. Pattern Anal. Mach. Intell. 32(8), 1502–1516 (2010). doi: 10.1109/TPAMI.2009.140

    Google Scholar 

  404. Proença, H., Alexandre, L.: Iris segmentation methodology for non-cooperative recognition. IEE Proceedings of Vis. Image Signal Process. 153(2), 199–205 (2006). doi: 10.1049/ ip-vis:20050213

    Google Scholar 

  405. Proença, H., Alexandre, L.: A method for the identification of noisy regions in normalized iris images. In: Proceedings of 18th International Conference on Pattern Recognition, vol. 4, pp. 405–408. IEEE, New York (2006). doi: 10.1109/ICPR.2006.100

    Google Scholar 

  406. Proença, H., Alexandre, L.: Toward noncooperative iris recognition: A classification approach using multiple signatures. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 607–612 (2007). doi: 10.1109/TPAMI.2007.1016

    Google Scholar 

  407. Proença, H., Alexandre, L.: Toward covert iris biometric recognition: Experimental results from the nice contests. IEEE Trans. Inform. Forensics Secur. 7(2), 798–808 (2012). doi: 10.1109/TIFS.2011.2177659

    Google Scholar 

  408. Proença, H., Alexandre, L.A.: Iris recognition: Analysis of the error rates regarding the accuracy of the segmentation stage. Image Vis. Comput. 28(1), 202–206 (2010). doi: 10.1016/j.imavis.2009.03.003

    Google Scholar 

  409. Proença, H., Filipe, S., Santos, R., Oliveira, J., Alexandre, L.A.: The ubiris.v2: A database of visible wavelength iris images captured on-the-move and at-a-distance. IEEE Trans. Pattern Anal. Mach. Intell. 32(8), 1529–1535 (2010). doi: 10.1109/TPAMI.2009.66

    Google Scholar 

  410. Puhan, N., Sudha, N., Jiang, X.: Robust eyeball segmentation in noisy iris images using fourier spectral density. In: Proceedings of 6th International Conference on Information, Communications and Signal Processing, pp. 1–5. IEEE, New York (2007). doi: 10.1109/ICICS.2007.4449723

    Google Scholar 

  411. Qi, M., Lu, Y., Du, N., Zhang, Y., Wang, C., Kong, J.: A novel image hiding approach based on correlation analysis for secure multimodal biometrics. J. Netw. Comput. Appl. 33(3), 247–257 (2010). doi: 10.1016/j.jnca.2009.12.004

    Google Scholar 

  412. Quan, F., Fei, S., Anni, C., Feifei, Z.: Cracking Cancelable Fingerprint Template of Ratha. In: Proceedings of International Symposium on Computer Science and Computational Technology, vol. 2, pp. 572–575. IEEE, New York (2008). doi: 10.1109/ISCSCT.2008.226

    Google Scholar 

  413. Qui, X., Sun, Z., Tan, T.: Coarse iris classification by learned visual dictionary. In: Lee, S.W., Li, S. (eds.) Proceedings of 2nd International Conference on Biometrics, LNCS, vol. 4642, pp. 770–779. Springer, New York (2007). doi: 10.1007/978-3-540-74549-5_81

    Google Scholar 

  414. Rajibul, M.I., Shohel, M., Andrews, S.: Biometric template protection using watermarking with hidden password encryption. In: Proceedings of International Symposium on Information Technology, pp. 296–303. IEEE, New York (2008). doi: 10.1109/ITSIM.2008.4631572

    Google Scholar 

  415. Rakshit, S., Monro, D.: Effects of sampling and compression on human iris verification. In: Proceedings of IEEE International Conference on Acustics, Speech, and Signal Processing, vol. 2, pp. 337–340. IEEE, New York (2006). doi: 10.1109/ICASSP.2006.1660348

    Google Scholar 

  416. Rakshit, S., Monro, D.M.: An evaluation of image sampling and compression for human iris recognition. IEEE Trans. Inform. Forensics Secur. 2, 605–612 (2007). doi: 10.1109/TIFS.2007.902401

    Google Scholar 

  417. Rakshit, S., Monro, D.M.: Pupil shape description using fourier series. In: Proceedings of IEEE Workshop on Signal Processing Applications for Public Security and Forensics, pp. 1–4. IEEE, New York (2007)

    Google Scholar 

  418. Ratha, N., Connell, J., Bolle, R.: Enhancing security and privacy in biometrics-based authentication systems. IBM Syst. J. 40(3), 614–634 (2001). doi: 10.1147/sj.403.0614

    Google Scholar 

  419. Ratha, N.K., Connell, J.H., Bolle, R.M.: Secure data hiding in wavelet compressed fingerprint images. In: Proceedings of ACM Multimedia, pp. 127–130. ACM, New York (2000). doi: 10.1145/357744.357902

    Google Scholar 

  420. Ratha, N.K., Connell, J.H., Bolle, R.M.: An analysis of minutiae matching strength. In: Bigün, J., Smeraldi, F. (eds.) Proceedings of 3rd International Conference on Audio- and Video-Based Biometric Person Authentication. LNCS, vol. 2091, pp. 223–228. Springer, New York (2001). doi: 10.1007/3-540-45344-X_32

    Google Scholar 

  421. Ratha, N.K., Connell, J.H., Bolle, R.M., Chikkerur, S.: Cancelable biometrics: A case study in fingerprints. In: Proceedings of 18th International Conference on Pattern Recognition, pp. 370–373. IEEE, New York (2006). doi: 10.1109/ICPR.2006.353

    Google Scholar 

  422. Ratha, N.K., Connell, J.H., Chikkerur, S.: Generating cancelable fingerprint templates. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 561–572 (2007). doi: 10.1109/TPAMI.2007.1004

    Google Scholar 

  423. Ratha, N.K., Figueroa-Villanueva, M.A., Connell, J.H., Bolle, R.M.: A secure protocol for data hiding in compressed fingerprint images. In: Maltoni, D., Jain, A. (eds.) Proceedings of International Workshop on Biometric Authentication. LNCS, vol. 3087, pp. 205–216. Springer, New York (2004). doi: 10.1007/978-3-540-25976-3_19

    Google Scholar 

  424. Rathgeb, C., Uhl, A.: Context-based texture analysis for secure revocable iris-biometric key generation. In: Proceedings of 3rd International Conference on Imaging for Crime Detection and Prevention, pp. 1–6. IET, London (2009). doi: 10.1049/ic.2009.0229

    Google Scholar 

  425. Rathgeb, C., Uhl, A.: An iris-based interval-mapping scheme for biometric key generation. In: Proceedings of 6th International Symposium on Image and Signal Processing and Analysis, pp. 511–516. IEEE, New York (2009)

    Google Scholar 

  426. Rathgeb, C., Uhl, A.: Adaptive fuzzy commitment scheme based on iris-code error analysis. In: Proceedings of 2nd European Workshop on Visual Information Processing, pp. 41–44. IEEE, New York (2010). doi: 10.1109/EUVIP.2010.5699103

    Google Scholar 

  427. Rathgeb, C., Uhl, A.: Bit reliability-driven template matching in iris recognition. In: Proceedings of 4th Pacific-Rim Symposium on Image and Video Technology, pp. 70–75. IEEE, New York (2010). doi: 10.1109/PSIVT.2010.19

    Google Scholar 

  428. Rathgeb, C., Uhl, A.: Iris-biometric hash generation for biometric database indexing. In: Proceedings of 20th International Conference on Pattern Recognition, pp. 2848–2851. IEEE, New York (2010). doi: 10.1109/ICPR.2010.698

    Google Scholar 

  429. Rathgeb, C., Uhl, A.: Privacy Preserving Key Generation for Iris Biometrics. In: Decker, B.D., Schaumüller-Bichl, I. (eds.) Proceedings of 11th International Conference on Communications and Multimedia Security. LNCS, vol. 6109, pp. 191–200. Springer, New York (2010). doi: 10.1007/978-3-642-13241-4_18

    Google Scholar 

  430. Rathgeb, C., Uhl, A.: Secure iris recognition based on local intensity variations. In: Campilho, A., Kamel, M. (eds.) Proceedings of 7th International Conference on Image Analysis and Recognition. LNCS, vol. 6112, pp. 266–275. Springer, New York (2010). doi: 10.1007/978-3-642-13775-4_27

    Google Scholar 

  431. Rathgeb, C., Uhl, A.: Two-Factor Authentication or How to Potentially Counterfeit Experimental Results in Biometric Systems. In: Campilho, A., Kamel, M. (eds.) Proceedings of 7th International Conference on Image Analysis and Recognition. LNCS, vol. 6112, pp. 296–305. Springer, New York (2010). doi: 10.1007/978-3-642-13775-4_30

    Google Scholar 

  432. Rathgeb, C., Uhl, A.: Context-based biometric key-generation for iris. IET Comput. Vis. 5(6), 389–397 (2011). doi: 10.1049/iet-cvi.2010.0176

    Google Scholar 

  433. Rathgeb, C., Uhl, A.: Image compression in iris-biometric fuzzy commitment schemes. Tech. Rep. 2011-05, University of Salzburg, Dept. of Computer Sciences (2011)

    Google Scholar 

  434. Rathgeb, C., Uhl, A.: The state-of-the-art in iris biometric cryptosystems. In: Yang, J., Nanni, L. (eds.) State of the art in Biometrics, pp. 179–202. InTech, New York (2011)

    Google Scholar 

  435. Rathgeb, C., Uhl, A.: Statistical attack against iris-biometric fuzzy commitment schemes. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshop, pp. 25–32. IEEE, New York (2011). doi: 10.1109/CVPRW.2011.5981720

    Google Scholar 

  436. Rathgeb, C., Uhl, A.: A survey on biometric cryptosystems and cancelable biometrics. EURASIP J. Inform. Secur. 2011(3) (2011). doi: doi:10.1186/1687-417X-2011-3

    Google Scholar 

  437. Rathgeb, C., Uhl, A.: Template protection under signal degradation: A case-study on iris-biometric fuzzy commitment schemes. Tech. Rep. 2011-04, University of Salzburg, Dept. of Computer Sciences (2011)

    Google Scholar 

  438. Rathgeb, C., Uhl, A., Wild, P.: Incremental iris recognition: A single-algorithm serial fusion strategy to optimize time complexity. In: Proceedings of IEEE 4th International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–6. IEEE, New York (2010). doi: 10.1109/BTAS.2010.5634475

    Google Scholar 

  439. Rathgeb, C., Uhl, A., Wild, P.: Iris-biometric comparators: Minimizing trade-offs costs between computational performance and recognition accuracy. In: Proceedings of 4th International Conference on Imaging for Crime Detection and Prevention, pp. 1–6. IET, London (2011). doi: 10.1049/ic.2011.0110

    Google Scholar 

  440. Rathgeb, C., Uhl, A., Wild, P.: On combining selective best bits of iris-codes. In: Vielhauer, C., Dittmann, J., Drygajlo, A., Juul, N., Fairhurst, M. (eds.) Proceedings of COST 2101 European Workshop on Biometrics and ID Management, LNCS, vol. 6583, pp. 227–237. Springer, New York (2011). doi: 10.1007/978-3-642-19530-3_21

    Google Scholar 

  441. Rathgeb, C., Uhl, A., Wild, P.: Reliability-balanced feature level fusion for fuzzy commitment scheme. In: Proceedings of International Joint Conference on Biometrics, pp. 1–7. IEEE, New York (2011). doi: 10.1109/IJCB.2011.6117535

    Google Scholar 

  442. Rathgeb, C., Uhl, A., Wild, P.: Shifting score fusion: On exploiting shifting variation in iris recognition. In: Proceedings of 26th ACM Symposium On Applied Computing, pp. 1–5. ACM, New York (2011). doi: 10.1145/1982185.1982187

    Google Scholar 

  443. Rathgeb, C., Uhl, A., Wild, P.: Iris-biometric comparators: Exploiting comparison scores towards an optimal alignment under gaussian assumption. In: Proceedings of 5th International Conference on Biometrics, pp. 297–302. IEEE, New York (2012). doi: 10.1109/ICB.2012.6199823

    Google Scholar 

  444. Ravidá, S.: WinUHA. URL http://www.klaimsoft.com/winuha/. Retrieved June 2012

  445. Reddy, E., Babu, I.: Performance of Iris Based Hard Fuzzy Vault. IJCSNS Int. J. Comput. Sci. Netw. Secur. 8(1), 297–304 (2008)

    Google Scholar 

  446. Reza, A.M.: Realization of the contrast limited adaptive histogram equalization (clahe) for real-time image enhancement. J. VLSI Signal Process. Syst. 38(1), 35–44 (2004). doi: 10.1023/B:VLSI.0000028532.53893.82

    Google Scholar 

  447. Ritter, N., Cooper, J.: Locating the iris: A first step to registration and identification. In: Proceedings of 9th IASTED International Conference on Signal and Image Processing, pp. 507–512. ACTA Press, Calgary, AB (2003)

    Google Scholar 

  448. Ritter, N., Owens, R., Van Saarloos, P.P., Cooper, J.: Location of the pupil-iris border in slit-lamp images of the cornea. In: Proceedings of 10th International Conference on Image Analysis and Processing, pp. 740–745. IEEE, New York (1999). doi: 10.1109/ICIAP.1999.797683

    Google Scholar 

  449. Roberts, C.: Biometric attack vectors and defenses. Comput. Secur. 26, 14–25 (2007). doi: 10.1016/j.cose.2006.12.008

    Google Scholar 

  450. Rosenfeld, K., Sencar, H.: A study of the robustness of prnu-based camera identification. In: Media Forensics and Security XI, Proceedings of SPIE, vol. 7254, pp. 72,540M.1–7. SPIE, Bellingham, WA (2009). doi: 10.1117/12.814705

    Google Scholar 

  451. Ross, A.: Information fusion in fingerprint authentication. Ph.D. thesis, Michigan State University (2003)

    Google Scholar 

  452. Ross, A.: Iris recognition: The path forward. Computer 43, 30–35 (2010). doi: 10.1109/MC.2010.44

    Google Scholar 

  453. Ross, A., Jain, A.K.: Information fusion in biometrics. Pattern Recogn. Lett. 24(13), 2115–2125 (2003). doi: 10.1016/S0167-8655(03)00079-5

    Google Scholar 

  454. Ross, A., Pasula, R., Hornak, L.: Exploring multispectral iris recognition beyond 900nm. In: Proceedings of IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–8. IEEE, New York (2009). doi: 10.1109/BTAS.2009.5339072

    Google Scholar 

  455. Ross, A., Shah, J., Jain, A.K.: From template to image: Reconstructing fingerprints from minutiae points. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 544–560 (2007). doi: 10.1109/TPAMI.2007.1018

    Google Scholar 

  456. Ross A.; Shah, S.: Segmenting non-ideal irises using geodesic active contours. In: Proceedings of Biometric Consortium Conference, pp. 1–6. IEEE, New York (2006). doi: 10.1109/BCC.2006.4341625

    Google Scholar 

  457. Rouse, D., Hemami, S.S.: Natural image utility assessment using image contours. In: Proceedings of IEEE International Conference on Image Processing, pp. 2217–2220. IEEE, New York (2009). doi: 10.1109/ICIP.2009.5413882

    Google Scholar 

  458. Rouse, D., Hemami, S.S.: The role of edge information to estimate the perceived utility of natural images. In: Western New York Image Processing Workshop, pp. 1–4. IEEE, New York (2009)

    Google Scholar 

  459. Roy, K., Suen, C., Bhattacharya, P.: Segmentation of unideal iris images using game theory. In: Proceedings of 20th International Conference on Pattern Recognition, pp. 2844–2847. IEEE, New York (2010). doi: 10.1109/ICPR.2010.697

    Google Scholar 

  460. Ryan, W., Woodard, D., Duchowski, A., Birchfield, S.: Adapting starburst for elliptical iris segmentation. In: Proceedings of IEEE 2nd International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–7. IEEE, New York (2008). doi: 10.1109/BTAS.2008.4699340

    Google Scholar 

  461. Rydgren, E., Ea, T., Amiel, F., Rossant, F., Amara, A.: Iris features extraction using wavelet packets. In: Proceedings of International Conference on Image Processing, vol. 2, pp. 861–864. IEEE, New York (2004). doi: 10.1109/ICIP.2004.1419435

    Google Scholar 

  462. SAFRAN Morpho, France: URL http://www.morpho.com/. Retrieved June 2012

  463. Said, A., Pearlman, W.A.: A new, fast, and efficient image codec based on set partitioning in hierarchical trees. IEEE Trans. Circ. Syst. Video Tech. 6(3), 243–249 (1996). doi: 10.1109/76.499834

    Google Scholar 

  464. Sankowski, W., Grabowski, K., Napieralska, M., Zubert, M., Napieralski, A.: Reliable algorithm for iris segmentation in eye image. Image Vis. Comput. 28(2), 231–237 (2010). doi: 10.1016/j.imavis.2009.05.014

    Google Scholar 

  465. Santa-Cruz, D., Grosbois, R., Ebrahimi, T.: JJ2000: The JPEG 2000 Reference Implementation in Java. In: Proceedings of the First International JPEG 2000 Workshop, 46–49 (2003)

    Google Scholar 

  466. Santos, G., Proenca, H.: On the role of interpolation in the normalization of non-ideal visible wavelength iris images. In: Proceedings of International Conference on Computational Intelligence and Security, vol. 1, pp. 315–319. IEEE, New York (2009). doi: 10.1109/CIS.2009.113

    Google Scholar 

  467. Saragih, J., Lucey, S., Cohn, J.: Face alignment through subspace constrained mean-shifts. In: Proceedings of International Conference on Computer Vision, pp. 1034–1041. IEEE, New York (2009). doi: 10.1109/ICCV.2009.5459377

    Google Scholar 

  468. Satonaka, T.: Biometric watermark authentication with multiple verification rule. In: Proceedings of 12th IEEE Workshop on Neural Networks in Signal Processing, pp. 597–606. IEEE, New York (2002). doi: 10.1109/NNSP.2002.1030071

    Google Scholar 

  469. Savvides, M., Kumar, B., Khosla, P.: Cancelable biometric filters for face recognition. In: Proceedings of 17th International Conference on Pattern Recognition, vol. 3, pp. 922–925. IEEE, New York (2004). doi: 10.1109/ICPR.2004.228

    Google Scholar 

  470. Scheidat, T., Vielhauer, C.: Biometric hashing for handwriting : Entropy based feature selection and semantic fusion. In: Delp, E., Wong, P., Dittmann, J., Memon, N. (eds.) Security, Forensics, Steganography, and Watermarking of Multimedia Contents X, Proceedings of SPIE, vol. 6819, pp. 68,190N.1–12. SPIE, Bellingham, WA (2008). doi: 10.1117/12.766378

    Google Scholar 

  471. Scheidat, T., Vielhauer, C., Dittmann, J.: An iris-based interval-mapping scheme for biometric key generation. In: Proceedings of 6th International Symposium on Image and Signal Processing and Analysis, pp. 550–555. IEEE, New York (2009)

    Google Scholar 

  472. Scheirer, W., Boult, T.: Cracking Fuzzy Vaults and Biometric Encryption. In: Proceedings of Biometrics Symposium, pp. 1–6. IEEE, New York (2007). doi: 10.1109/BCC.2007.4430534

    Google Scholar 

  473. Schimke, S., Vielhauer, C., Dittmann, J.: Using adapted levenshtein distance for on-line signature authentication. In: Proceedings of 17th International Conference on Pattern Recognition, pp. 931–934. IEEE, New York (2004). doi: 10.1109/ICPR.2004.965

    Google Scholar 

  474. Schiphol: Iris scans at Amsterdam Airport Schiphol. URL http://www.schiphol.nl/Travellers/AtSchiphol/Privium/IrisScans.htm. Retrieved May 2012

  475. Schuckers, S., Hornak, L., Norman, T., Derakhshani, R., Parthasaradhi, S.: Issues for liveness detection in biometrics. In: Proceedings of Biometric Consortium Conference. IEEE, New York (2002)

    Google Scholar 

  476. Schuckers, S., Schmid, N., Abhyankar, A., Dorairaj, V., Boyce, C., Hornak, L.: On techniques for angle compensation in nonideal iris recognition. IEEE Trans. Syst. Man Cyben. B Cybern. 37(5), 1176–1190 (2007). doi: 10.1109/TSMCB.2007.904831

    Google Scholar 

  477. Securics Inc., USA: URL http://www.securics.com/. Retrieved June 2012

  478. Shah, S., Ross, A.: Iris segmentation using geodesic active contours. IEEE Trans. Inform. Forensics Secur. 4(4), 824–836 (2009). doi: 10.1109/TIFS.2009.2033225

    Google Scholar 

  479. Sheikh, H.R., Bovik, A.C.: Image information and visual quality. IEEE Trans. Image Process. 15(2), 430–444 (2006). doi: 10.1109/TIP.2005.859378

    Google Scholar 

  480. Shin, S.W., Lee, M.K., Moon, D., Moon, K.: Dictionary attack on functional transform-based cancelable fingerprint templates. ETRI J. 31(5), 628–630 (2009). doi: 10.4218/etrij.09.0209.0137

    Google Scholar 

  481. Simoens, K., Tuyls, P., Preneel, B.: Privacy weaknesses in biometric sketches. In: Proceedings of 30th IEEE Symposium on Security and Privacy, pp. 188–203. IEEE, New York (2009). doi: 10.1109/SP.2009.24

    Google Scholar 

  482. Skiljan, I.: IrfanView. URL http://irfanview.tuwien.ac.at/. Retrieved June 2012

  483. SOCIA Lab, University of Beira Interior: Noisy Iris Challenge Evaluation Part I. URL http://nice1.di.ubi.pt/. Retrieved May 2012

  484. SOCIA Lab, University of Beira Interior: Noisy Iris Challenge Evaluation Part II. URL http://nice2.di.ubi.pt/. Retrieved May 2012

  485. SOCIA Lab, University of Beira Interior: UBIRIS.v1 Database. URL http://iris.di.ubi.pt/ubiris1.html. Retrieved May 2012

  486. SOCIA Lab, University of Beira Interior: UBIRIS.v2 Database. URL http://iris.di.ubi.pt/ubiris2.html. Retrieved May 2012

  487. Song, O.T., Teoh, A.B., Ngo, D.C.L.: Application-specific key release scheme from biometrics. Int. J. Netw. Secur. 6(2), 122–128 (2008)

    Google Scholar 

  488. Soutar, C., Roberge, D., Stoianov, A., Gilroy, R., Kumar, B.V.: Biometric Encryption - Enrollment and Verification Procedures. In: Casasent, D., Chao, T.H. (eds.) Optical Pattern Recognition IX, Proceedings of SPIE, vol. 3386, pp. 24–35. SPIE, Bellingham, WA (1998). doi: 10.1117/12.304770

    Google Scholar 

  489. Soutar, C., Roberge, D., Stoianov, A., Gilroy, R., Kumar, B.V.: Biometric Encryption using image processing. In: Renesse, R.V. (ed.) Optical Security and Counterfeit Deterrence Techniques II, Proceedings of SPIE, vol. 3314, pp. 178–188. SPIE, Bellingham, WA (1998). doi: 10.1117/12.304705

    Google Scholar 

  490. Soutar, C., Roberge, D., Stoianov, A., Gilroy, R., Kumar, B.V.: Biometric encryption. In: ICSA Guide to Cryptography, pp. 1–28. McGraw-Hill, New York (1999)

    Google Scholar 

  491. Soutar, C., Roberge, D., Stoianov, A., Gilroy, R., Kumar, B.V.: Method for secure key management using a biometrics (2001). U.S. Patent 6219794

    Google Scholar 

  492. Soutar, C., Tomko, G.J., Schmidt, G.J.: Fingerprint controlled public key cryptographic system (1996). U.S. Patent 5541994

    Google Scholar 

  493. SRI Sarnoff: IOM N-Glance. URL http://www.sarnoff.com/products/iris-on-the-move/compact-system. Retrieved May 2012

  494. SRI Sarnoff: IOM PassPort. URL http://www.sarnoff.com/products/iris-on-the-move/portal-system. Retrieved May 2012

  495. SRI Sarnoff: IOM RapID-Cam II. URL http://www.sarnoff.com/products/iris-on-the-move/handheld-system. Retrieved May 2012

  496. Stögner, H., Uhl, A., Weinhandel, G.: Experiments on improving lossless compression of biometric iris sample data. In: Zovko-Cihlar, B., Behlilovic, N., Hadzialic, M. (eds.) Proceedings of 18th International Conference on Systems, Signals and Image Processing, pp. 217–220. IEEE, New York (2011)

    Google Scholar 

  497. Stoianov, A., Kevenaar, T., van der Veen, M.: Security issues of biometric encryption. In: Proceedings of Toronto International Conference on Science and Technology for Humanity, pp. 34–39. IEEE, New York (2009). doi: 10.1109/TIC-STH.2009.5444478

    Google Scholar 

  498. Storer, J.: Image and Text Compression. The Kluwer international series in engineering and computer science. Kluwer, Dordrecht (1992)

    Google Scholar 

  499. Sun, Z., Tan, T., Wang, Y.: Robust encoding of local ordinal measures: A general framework of iris recognition. In: Maltoni, D., Jain, A. (eds.) Proceedings of ECCV Workshop BioAW, LNCS, vol. 3087, pp. 270–282. Springer, New York (2004). doi: 10.1007/978-3-540-25976-3_25

    Google Scholar 

  500. Sun, Z., Wang, Y., Tan, T., Cui, J.: Improving iris recognition accuracy via cascaded classifiers. IEEE Trans. Syst. Man Cybern. C Appl. Rev. 35(3), 435–441 (2005). doi: 10.1109/TSMCC.2005.848169

    Google Scholar 

  501. Sutcu, Y., Li, Q., Memon, N.: How to Protect Biometric Templates. In: Delp, E., Wong, P. (eds.) Conference on Security, Steganography and Watermarking of Multimedia Contents IX, Proceedings of SPIE, vol. 6505, pp. 650514.1–11. SPIE, Bellingham, WA (2007). doi: 10.1117/12.705896

    Google Scholar 

  502. Sutcu, Y., Li, Q., Memon, N.: Protecting biometric templates with sketch: Theory and practice. IEEE Trans. Inform. Forensics Secur. 2, 503–512 (2007). doi: 10.1109/TIFS.2007.902022

    Google Scholar 

  503. Sutcu, Y., Li, Q., Memon, N.: Secure biometric templates from fingerprint-face features. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–6. IEEE, New York (2007). doi: 10.1109/CVPR.2007.383385

    Google Scholar 

  504. Sutcu, Y., Sencar, H.T., Memon, N.: A secure biometric authentication scheme based on robust hashing. In: Proceedings of 7th Workshop on Multimedia and Security, pp. 111–116. ACM, New York (2005). doi: 10.1145/1073170.1073191

    Google Scholar 

  505. Szewczyk, R., Grabowski, K., Napieralska, M., Sankowski, W., Zubert, M., Napieralski, A.: A reliable iris recognition algorithm based on reverse biorthogonal wavelet transform. Pattern Recogn. Lett. 33(8), 1019–1026 (2012). doi: 10.1016/j.patrec.2011.08.018

    Google Scholar 

  506. Tabassi, E., Grother, P., Salamon, W.: Irex ii - iqce iris quality calibration and evaluation. Interagency report 7820, NIST (2011)

    Google Scholar 

  507. Tajbakhsh, N., Araabi, B.N., Soltanian-Zadeh, H.: Robust iris verification based on local and global variations. EURASIP J. Adv. Signal Process. 2010, 70:1–70:12 (2010). doi: 10.1155/2010/979058

    Google Scholar 

  508. Takahashi, K., Hirata, S.: Generating provably secure cancelable fingerprint templates based on correlation-invariant random filtering. In: Proceedings of IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–6. IEEE, New York (2009). doi: 10.1109/BTAS.2009.5339047

    Google Scholar 

  509. Tan, C.W., Kumar, A.: Automated segmentation of iris images using visible wavelength face images. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshop, pp. 9–14. IEEE, New York (2011). doi: 10.1109/CVPRW.2011.5981682

    Google Scholar 

  510. Tan, T., He, Z., Sun, Z.: Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition. Image Vis. Comput. 28(2), 223–230 (2010). doi: 10.1016/j.imavis.2009.05.008

    Google Scholar 

  511. Tan, T., Zhang, X., Sun, Z., Zhang, H.: Noisy iris image matching by using multiple cues. Pattern Recogn. Lett. 33(8), 970–977 (2012). doi: 10.1016/j.patrec.2011.08.009

    MathSciNet  Google Scholar 

  512. Tao, Z., Ming-Yu, F., Bo, F.: Side-channel attack on biometric cryptosystem based on keystroke dynamics. In: Proceedings of 1st International Symposium on Data, Privacy, and E-Commerce, pp. 221–223. IEEE, New York (2007). doi: 10.1109/ISDPE.2007.48

    Google Scholar 

  513. Taubin, G.: Estimation of planar curves, surfaces, and nonplanar space curves defined by implicit equations with applications to edge and range image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 13(11), 1115–1138 (1991). doi: 10.1109/34.103273

    Google Scholar 

  514. Taubman, D., Marcellin, M.: JPEG2000 — Image Compression Fundamentals, Standards and Practice. Kluwer, Dordrecht (2002)

    Google Scholar 

  515. Teoh, A., Kim, J.: Secure biometric template protection in fuzzy commitment scheme. IEICE Electron. Express 4(23), 724–730 (2007)

    Google Scholar 

  516. Teoh, A.B.J., Chong, L.Y.: Secure speech template protection in speaker verification system. Speech Comm. 52(2), 150–163 (2010). doi: 10.1016/j.specom.2009.09.003

    Google Scholar 

  517. Teoh, A.B.J., Kuan, Y.W., Lee, S.: Cancellable biometrics and annotations on biohash. Pattern Recogn. 41(6), 2034–2044 (2008). doi: 10.1016/j.patcog.2007.12.002

    MATH  Google Scholar 

  518. Teoh, A.B.J., Ngo, D.C.L.: Biophasor: Token supplemented cancellable biometrics. In: Proceedings of International Conference on Control, Automation, Robotics and Vision, pp. 1–5. IEEE, New York (2006). doi: 10.1109/ICARCV.2006.345404

    Google Scholar 

  519. Teoh, A.B.J., Ngo, D.C.L., Goh, A.: Biohashing: two factor authentication featuring fingerprint data and tokenised random number. Pattern Recogn. 37(11), 2245–2255 (2004). doi: 10.1016/j.patcog.2004.04.011

    Google Scholar 

  520. Teoh, A.B.J., Ngo, D.C.L., Goh, A.: Personalised cryptographic key generation based on FaceHashing. Comput. Secur. 2004(23), 606–614 (2004). doi: 10.1016/j.cose.2004.06.002

    Google Scholar 

  521. Teoh, A.B.J., Ngo, D.C.L., Goh, A.: Biometric Hash: High-Confidence Face Recognition. IEEE Trans. Circ. Syst. Video Tech. 16(6), 771–775 (2006). doi: 10.1109/TCSVT.2006.873780

    Google Scholar 

  522. Teoh, A.B.J., Yuang, C.T.: Cancellable biometrics realization with multispace random projections. IEEE Trans. Syst. Man Cybern. B Cybern. 37(5), 1096–1106 (2007). doi: 10.1109/TSMCB.2007.903538

    Google Scholar 

  523. Thärna, J., Nilsson, K., Bigun, J.: Orientation scanning to improve lossless compression of fingerprint images. In: Kittler, J., Nixon, M. (eds.) Proceedings of 4th International Conference on Audio- and Video-Based Biometric Person Authentication. LNCS, vol. 2688, pp. 343–350. Springer, New York (2003). doi: 10.1007/3-540-44887-X_41

    Google Scholar 

  524. Thoonsaengngam, P., Horapong, K., Thainimit, S., Areekul, V.: Efficient iris recognition using adaptive quotient thresholding. In: Zhang, D., Jain, A. (eds.) Proceedings of 1st International Conference on Biometrics, LNCS, vol. 3832, pp. 472–478. Springer, New York (2006). doi: 10.1007/11608288_63

    Google Scholar 

  525. Tisse, C., Martin, L., Torres, L., Robert, M.: Person identification technique using human iris recognition. In: Proceedings of Vision Interface, pp. 294–299 (2002)

    Google Scholar 

  526. Tistarelli, M., Nixon, M. (eds.): Systematic Construction of Iris-based Fuzzy Commitment Schemes. LNCS, vol. 5558. Springer, New York (2009). doi: 10.1007/978-3-642-01793-3_95

    Google Scholar 

  527. Tong, L., Dai, F., Zhang, Y., Li, J.: Visual security evaluation for video encryption. In: Proceedings of International Conference on Multimedia, pp. 835–838. ACM, New York (2010). doi: 10.1145/1873951.1874091

    Google Scholar 

  528. Tong, V., Sibert, H., Lecoeur, J., Girault, M.: Biometric fuzzy extractors made practical: a proposal based on fingercodes. In: Lee, S.W., Li, S. (eds.) Proceedings of 2nd International Conference on Biometrics, LNCS, vol. 4642, pp. 604–613. Springer, New York (2007). doi: 10.1007/978-3-540-74549-5_64

    Google Scholar 

  529. Trappe, W., Washington, L.C.: Introduction to Cryptography with Coding Theory: 2nd Edition. Pearson Prentice Hall, Upper Saddle River (2006)

    MATH  Google Scholar 

  530. Tuceryan, M.: Moment based texture segmentation. In: Proceedings of 11th International Conference on Pattern Recognition, pp. 45–48. IEEE, New York (1992). doi: 10.1109/ICPR.1992.201924

    Google Scholar 

  531. Tulyakov, S., Farooq, F., Govindaraju, V.: Symmetric hash functions for fingerprint minutiae. In: Maltoni, D., Jain, A. (eds.) Proceedings of 3rd International Conference on Advances in Pattern Recognition. LNCS, vol. 3687, pp. 30–38. Springer, New York (2005). doi: 10.1007/11552499_4

    Google Scholar 

  532. Tulyakov, S., Farooq, F., Mansukhani, P., Govindaraju, V.: Symmetric hash functions for secure fingerprint biometric systems. Pattern Recogn. Lett. 28(16), 2427–2436 (2007). doi: 10.1016/j.patrec.2007.08.008

    Google Scholar 

  533. Tuyls, P., Akkermans, A.H.M., Kevenaar, T.A.M., Schrijen, G.J., Bazen, A.M., Veldhuis, R.N.J.: Practical biometric authentication with template protection. In: Kanade, T., Jain, A., Ratha, N. (eds.) Proceedings of 5th International Conference on Audio- and Video-Based Biometric Person Authentication. LNCS, vol. 3546, pp. 436–446. Springer, New York (2005). doi: 10.1007/11527923_45

    Google Scholar 

  534. Tuyls, P., Goseling, J.: Capacity and examples of template-protecting biometric authentication systems. In: Maltoni, D., Jain, A. (eds.) Proceedings of International Workshop on Biometric Authentication. LNCS, vol. 3087, pp. 158–170. Springer, New York (2004). doi: 10.1007/978-3-540-25976-3_15

    Google Scholar 

  535. Uhl, A., Höller, Y.: Iris-sensor authentication using camera PRNU fingerprints. In: Proceedings of 5th International Conference on Biometrics, pp. 230–237. IEEE, New York (2012). doi: 10.1109/ICB.2012.6199813

    Google Scholar 

  536. Uhl, A., Wild, P.: Parallel versus serial classifier combination for multibiometric hand-based identification. In: Tistarelli, M., Nixon, M. (eds.) Proceedings of 3rd International Conference on Biometrics. LNCS, vol. 5558, pp. 950–959. Springer, New York (2009). doi: 10.1007/978-3-642-01793-3_96

    Google Scholar 

  537. Uhl, A., Wild, P.: Single-sensor multi-instance fingerprint and eigenfinger recognition using (weighted) score combination methods. Int. J. Biometrics 1(4), 442–462 (2009). doi: 10.1504/IJBM.2009.027305

    Google Scholar 

  538. Uhl, A., Wild, P.: Enhancing iris matching using levenshtein distance with alignment constraints. In: Bebis, G., Boyle, R.D., Parvin, B., Koracin, D., Chung, R., Hammoud, R.I., Hussain, M., Tan, K.H., Crawfis, R., Thalmann, D., Kao, D., Avila, L. (eds.) Proceedings of 6th International Symposium on Advances in Visual Computing. LNCS, vol. 6453, pp. 469–479. Springer, New York (2010). doi: 10.1007/978-3-642-17289-2_45

    Google Scholar 

  539. Uhl, A., Wild, P.: Combining face with face-part detectors under gaussian assumption. In: Campilho, A., Kamel, M. (eds.) Proceedings of 9th International Conference on Image Analysis and Recognition. LNCS, vol. 7325, pp. 80–89. Springer, New York (2012)

    Google Scholar 

  540. Uhl, A., Wild, P.: Multi-stage visible wavelength and near infrared iris segmentation framework. In: Campilho, A., Kamel, M. (eds.) Proceedings of 9th International Conference on Image Analysis and Recognition. LNCS, vol. 7325, pp. 1–10. Springer, New York (2012)

    Google Scholar 

  541. Uhl, A., Wild, P.: Weighted adaptive hough and ellipsopolar transforms for real-time iris segmentation. In: Proceedings of 5th International Conference on Biometrics, pp. 283–290. IEEE, New York (2012). doi: 10.1109/ICB.2012.6199821

    Google Scholar 

  542. UK Border Agency: Iris recognition immigration system. URL http://www.ukba.homeoffice.gov.uk/customs-travel/Enteringtheuk/usingiris/. Retrieved May 2012

  543. Uludag, U., Günsel, B., Ballan, M.: A spatial method for watermarking of fingerprint images. In: Proceedings of 1st International Workshop on Pattern Recognition in Information Systems, pp. 26–33. ICEIS Press (2001)

    Google Scholar 

  544. Uludag, U., Jain, A.K.: Fuzzy fingerprint vault. In: Proceedings of Workshop Biometrics: Challenges Arising from Theory to Practice, pp. 13–16 (2004)

    Google Scholar 

  545. Uludag, U., Jain, A.K.: Securing fingerprint template: Fuzzy vault with helper data. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshop, p. 163. IEEE, New York (2006). doi: 10.1109/CVPRW.2006.185

    Google Scholar 

  546. Uludag, U., Pankanti, S., Prabhakar, S., Jain, A.K.: Biometric cryptosystems: issues and challenges. Proc. IEEE 92(6), 948–960 (2004). doi: 10.1109/JPROC.2004.827372

    Google Scholar 

  547. UNHCR: Iris testing of returning Afghans passes 200,000 mark. URL http://www.unhcr.org/cgi-bin/texis/vtx/search?docid=3f86b4784. Retrieved May 2012

  548. Unique Identification Authority of India: Aadhaar. URL http://uidai.gov.in/. Retrieved May 2012

  549. Unique Identification Authority of India: Role of Biometric Technology in Aadhaar Enrollment. URL http://uidai.gov.in/images/FrontPageUpdates/role\_of\_biometric\_technology\_in\_aadhaar\_jan21\_2012.pdf. Retrieved May 2012

  550. University of Bath: Bath Iris Image Database. URL http://www.smartsensors.co.uk/information/bath-iris-image-database. Retrieved May 2012

  551. Upmanyu, M., Namboodiri, A.M., Srinathan, K., Jawahar, C.: Efficient privacy preserving video surveillance. In: Proceedings of IEEE International Conference on Computer Vision, pp. 1639–1646. IEEE, New York (2009). doi: 10.1109/ICCV.2009.5459370

    Google Scholar 

  552. US Department of Homeland Security: Independent Testing of Iris Recognition Technology (ITIRT), Report NBCHC030114/0002 (2005). URL http://www.hsdl.org/?view\&did=464567. Retrieved May 2012

  553. Vatsa, M., Singh, R., Gupta, P.: Comparison of iris recognition algorithms. In: Proceedings of International Conference on Intelligent Sensing and Information Processing, pp. 354–358. IEEE, New York (2004). doi: 10.1109/ICISIP.2004.1287682

    Google Scholar 

  554. Vatsa, M., Singh, R., Noore, A.: Improving biometric recognition accuracy and robustness using DWT and SVM watermarking. IEICE Electron. Express 2(12), 362–367 (2005)

    Google Scholar 

  555. Vatsa, M., Singh, R., Noore, A.: Reducing the false rejection rate of iris recognition using textural and topological features. Int. J. Signal Process. 2(2), 66–72 (2005)

    Google Scholar 

  556. Vatsa, M., Singh, R., Noore, A.: Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing. IEEE Trans. Syst. Man Cybern. B Cybern. 38(4), 1021–1035 (2008). doi: 10.1109/TSMCB.2008.922059

    Google Scholar 

  557. Vatsa, M., Singh, R., Noore, A.: Feature based RDWT watermarking for multimodal biometric system. Image Vis. Comput. 27(3), 293–304 (2009). doi: 10.1016/j.imavis.2007.05.003

    Google Scholar 

  558. Vatsa, M., Singh, R., Noore, A., Houck, M., Morris, K.: Robust biometric image watermarking for fingerprint and face template protection. IEICE Electron. Express 3(2), 23–28 (2006)

    Google Scholar 

  559. Vielhauer, C.: Biometric User Authentication for IT Security, vol. 18. Springer, New York (2006). doi: 10.1007/0-387-28094-4

    Google Scholar 

  560. Vielhauer, C., Steinmetz, R.: Approaches to biometric watermarks for owner authentification. In: Wong, P., Delp, E. (eds.) Security and Watermarking of Multimedia Contents III, Proceedings of SPIE, vol. 4314, pp. 209–219. SPIE, Bellingham, WA (2001). doi: 10.1117/12.435401

    Google Scholar 

  561. Vielhauer, C., Steinmetz, R.: Handwriting: feature correlation analysis for biometric hashes. EURASIP J. Appl. Signal Process. 2004(1), 542–558 (2004). doi: 10.1155/S1110865704309248

    Google Scholar 

  562. Vielhauer, C., Steinmetz, R., Mayerhöfer, A.: Biometric hash based on statistical features of online signatures. In: Proceedings of 16th International Conference on Pattern Recognition, pp. 123–126. IEEE, New York (2002). doi: 10.1109/ICPR.2002.1044628

    Google Scholar 

  563. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 511–518. IEEE, New York (2001). doi: 10.1109/CVPR.2001.990517

    Google Scholar 

  564. Viola, P., Jones, M.J.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004). doi: 10.1023/B:VISI.0000013087.49260.fb

    Google Scholar 

  565. Voderhobli, K., Pattinson, C., Donelan, H.: A schema for cryptographic key generation using hybrid biometrics. In: Proceedings of 7th Symposium on the Convergence of Telecommunications, Networking and Broadcasting, pp. 1–6 (2006)

    Google Scholar 

  566. Wallace, G.: The JPEG still picture compression standard. Comm. ACM 34(4), 30–44 (1991). doi: 10.1145/103085.103089

    Google Scholar 

  567. Wang, D.S., Li, J.P., Hu, D.K., Yan, Y.H.: A Novel Biometric Image Integrity Authentication Using Fragile Watermarking and Arnold Transform. In: Li, J., Bloshanskii, I., Ni, L., Pandey, S.S., Yang, S.X. (eds.) Proceedings of International Conference on Information Computing and Automatation, pp. 799–802. World Scientific Publishing, Singapore (2007). doi: 10.1142/9789812799524_0203

    Google Scholar 

  568. Wang, F., Han, J., Yao, X.: Iris recognition based on multialgorithmic fusion. WSEAS Trans. Inform. Sci. Appl. 12(4), 1415–1421 (2007)

    Google Scholar 

  569. Wang, K., Qian, Y.: Fast and accurate iris segmentation based on linear basis function and ransac. In: Proceedings of 18th IEEE International Conference on Image Processing, pp. 3205–3208. IEEE, New York (2011). doi: 10.1109/ICIP.2011.6116350

    Google Scholar 

  570. Wang, P., Green, M., Ji, Q., Wayman, J.: Automatic eye detection and its validation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 164–171. IEEE, New York (2005). doi: 10.1109/CVPR.2005.570

    Google Scholar 

  571. Wang, Y., Plataniotis, K.: Face based biometric authentication with changeable and privacy preservable templates. In: Proceedings of Biometrics Symposium, pp. 11–13. IEEE, New York (2007). doi: 10.1109/BCC.2007.4430530

    Google Scholar 

  572. Wang, Y., Tan, T., Jain, A.: Combining face and iris biometrics for identity verification. In: Kittler, J., Nixon, M. (eds.) Proceedings of 4th International Conference on Audio- and Video-Based Biometric Person Authentication. LNCS, vol. 2688, pp. 805–813. Springer, New York (2003). doi: 10.1007/3-540-44887-X_93

    Google Scholar 

  573. Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004). doi: 10.1109/TIP.2003.819861

    Google Scholar 

  574. Wayman, J., Orlans, N., Hu, Q., Goodman, F., Ulrich, A., Valencia, V.: Technology assessment for the state of the art biometrics excellence roadmap vol. 2 ver. 1.3. Tech. rep., The MITRE Corporation (2009). US Gov Contr. J-FBI-07-164

    Google Scholar 

  575. Wayman, J.L.: Technical testing and evaluation of biometric identification devices. In: Biometrics: Personal Identification in a Networked Society, pp. 345–368. Kluwer, Dordrecht (1999)

    Google Scholar 

  576. Weinberger, M., Seroussi, G., Sapiro, G.: The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS. IEEE Trans. Image Process. 9(8), 1309–1324 (2000). doi: 10.1109/83.855427

    Google Scholar 

  577. Weinhandel, G., Stögner, H., Uhl, A.: Experimental study on lossless compression of biometric sample data. In: Proceedings of 6th International Symposium on Image and Signal Processing and Analysis, pp. 517–522. IEEE, New York (2009)

    Google Scholar 

  578. Wheeler, F., Perera, A., Abramovich, G., Yu, B., Tu, P.: Stand-off iris recognition system. In: Proceedings of IEEE 2nd International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–7. IEEE, New York (2008). doi: 10.1109/BTAS.2008.4699381

    Google Scholar 

  579. Wildes, R.: Iris recognition: an emerging biometric technology. Proc. IEEE 85(9), 1348–1363 (1997). doi: 10.1109/5.628669

    Google Scholar 

  580. Wildes, R.P., Asmuth, J.C., Green, G.L., Hsu, S.C., Kolczynski, R.J., Matey, J.R., McBride, S.E.: A machine-vision system for iris recognition. Mach. Vis. Appl. 9(1), 1–8 (1996). doi: 10.1007/BF01246633

    Google Scholar 

  581. Willems, F., Ignatenko, T.: Identification and secret-key binding in binary-symmetric template-protected biometric systems. In: Proceedings of IEEE International Workshop on Information Forensics and Security, pp. 1–5. IEEE, New York (2010). doi: 10.1109/WIFS.2010.5711455

    Google Scholar 

  582. Wolberg, G.: Image morphing: a survey. Vis. Comput. 14(8/9), 360–372 (1998)

    Google Scholar 

  583. Wu, G.D., Huang, P.H.: Image watermarking using structure based wavelet tree quantization. In: Proceedings of 6th IEEE/ACIS International Conference on Computer and Information Science, pp. 315–319. IEEE, New York (2007). doi: 10.1109/ICIS.2007.111

    Google Scholar 

  584. Wu, X., Qi, N., Wang, K., Zhang, D.: An iris cryptosystem for information security. In: Proceedings of 2008 International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 1533–1536. IEEE, New York (2008). doi: 10.1109/IIH-MSP.2008.83

    Google Scholar 

  585. Wu, X., Qi, N., Wang, K., Zhang, D.: A novel cryptosystem based on iris key generation. In: Proceedings of 4th International Conference on Natural Computation, pp. 53–56. IEEE, New York (2008). doi: 10.1109/ICNC.2008.808

    Google Scholar 

  586. Wu, X., Wang, K., Zhang, D.: A cryptosystem based on palmprint feature. In: Proceedings of 19th International Conference on Pattern Recognition, pp. 1–4. IEEE, New York (2008). doi: 10.1109/ICPR.2008.4761117

    Google Scholar 

  587. Wu, Y., Qiu, B.: Transforming a pattern identifier into biometric key generators. In: Proceedings of International Conference on Multimedia and Expo, pp. 78–82. IEEE, New York (2010). doi: 10.1109/ICME.2010.5583388

    Google Scholar 

  588. Xiao, Q.: Face detection using information fusion. In: IEEE Workshop on Comp. Intell. in Biometrics and Identity Mgmnt., pp. 157–162 (2011)

    Google Scholar 

  589. Xie, L., Arce, G.R.: Joint wavelet compression and authentication watermarking. In: Proceedings of IEEE International Conference on Image Processing, vol. 2, pp. 427–431. IEEE, New York (1998). doi: 10.1109/ICIP.1998.723409

    Google Scholar 

  590. Xu, G., Zhang, Z., Ma, Y.: A novel method for iris feature extraction based on intersecting cortical model network. J. Appl. Math. Comput. 26, 341–352 (2008). doi: 10.1007/s12190-007-0035-y

    MathSciNet  Google Scholar 

  591. Xu, H., Veldhuis, R.N.: Binary representations of fingerprint spectral minutiae features. In: Proceedings of 20th International Conference on Pattern Recognition, pp. 1212–1216. IEEE, New York (2010). doi: 10.1109/ICPR.2010.302

    Google Scholar 

  592. Xu, Z., Shi, P.: A robust and accurate method for pupil features extraction. In: Proceedings of 18th International Conference on Pattern Recognition, pp. 437–440. IEEE, New York (2006). doi: 10.1109/ICPR.2006.165

    Google Scholar 

  593. Yang, B., Busch, C., Gafurov, D., Bours, P.: Renewable minutiae templates with tunable size and security. In: Proceedings of 20th International Conference on Pattern Recognition, pp. 878–881. IEEE, New York (2010). doi: 10.1109/ICPR.2010.221

    Google Scholar 

  594. Yang, B., Hartung, D., Simoens, K., Busch, C.: Dynamic random projection for biometric template protection. In: Proceedings of IEEE 4th International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–7. IEEE, New York (2010). doi: 10.1109/BTAS.2010.5634538

    Google Scholar 

  595. Yang, K., Du, E.: A multi-stage approach for non-cooperative iris recognition. In: Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, pp. 3386–3391 (2011). doi: 10.1109/ICSMC.2011.6084192

    Google Scholar 

  596. Yang, S., Verbauwhede, I.: Automatic secure fingerprint verification system based on fuzzy vault scheme. In: Proceedings of IEEE International Conference Audio, Speech and Signal Processing, vol. 5, pp. 609–6012. IEEE, New York (2005). doi: 10.1109/ICASSP.2005.1416377

    Google Scholar 

  597. Yang, S., Verbauwhede, I.: Secure Iris Verification. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 2, pp. 133–136. IEEE, New York (2007). doi: 10.1109/ICASSP.2007.366190

    Google Scholar 

  598. Yeung, M.M., Pankanti, S.: Verification watermarks on fingerprint recognition and retrieval. In: Wong, P.W., Delp, E.J. (eds.) Security and Watermarking of Multimedia Contents, Proceedings of SPIE, vol. 3657, pp. 66–78. SPIE, Bellingham, WA (1999). doi: 10.1117/12.344704

    Google Scholar 

  599. Yeung, M.M., Pankanti, S.: Verification watermarks on fingerprint recognition and retrieval. J. Electron. Imag. 9(4), 468–476 (2000). doi: 10.1117/1.1287795

    Google Scholar 

  600. Yip, W.K., Teoh, A.B.J., Ngo, D.C.L.: Replaceable and securely hashed keys from online signatures. IEICE Electron. Express 3(18), 410–416 (2006). doi: 10.1587/elex.3.410

    Google Scholar 

  601. Yu, L., Wang, K., Zhang, D.: A novel method for coarse iris classification. In: Zhang, D., Jain, A. (eds.) Proceedings of 1st International Conference on Biometrics, LNCS, vol. 3832, pp. 404–410. Springer, New York (2006). doi: 10.1007/11608288_54

    Google Scholar 

  602. Zaim, A.: Automatic segmentation of iris images for the purpose of identification. In: Proceedings of IEEE International Conference on Image Processing, vol. 3, pp. 273–276. IEEE, New York (2005). doi: 10.1109/ICIP.2005.1530381

    Google Scholar 

  603. Zebbiche, K., Ghouti, L., Khelifi, F., Bouridane, A.: Protecting fingerprint data using watermarking. In: Proceedings of 1st NASA/ESA Conference on Adaptive Hardware and Systems, pp. 451–456. IEEE, New York (2006). doi: 10.1109/AHS.2006.61

    Google Scholar 

  604. Zebbiche, K., Khelifi, F.: Region-based watermarking of biometric images: Case study in fingerprint images. Int. J. Digit. Multimed. Broadcast. 2008, 492942.1–13 (2008). doi: 10.1155/2008/492942

    Google Scholar 

  605. Zebbiche, K., Khelifi, F., Bouridane, A.: An efficient watermarking technique for the protection of fingerprint images. EURASIP J. Inform. Secur. 2008, 918601.1–20 (2008). doi: 10.1155/2008/918601

    Google Scholar 

  606. Zeitz, C., Scheidat, T., Dittmann, J., Vielhauer, C.: Security issues of internet-based biometric authentication systems: risks of man-in-the-middle and BioPhishing on the example of BioWebAuth. In: Delp, E., Wong, P., Dittmann, J., Memon, N. (eds.) Security, Forensics, Steganography, and Watermarking of Multimedia Contents X, Proceedings of SPIE, vol. 6819, pp. 68190R.1–12. SPIE, Bellingham, WA (2008). doi: 10.1117/12.767632

    Google Scholar 

  607. Zeng, Z., Watters, P.A.: A novel face hashing method with feature fusion for biometric cryptosystems. In: Proceedings of European Conference on Universal Multiservice Networks, pp. 439–444. IEEE, New York (2007). doi: 10.1109/ECUMN.2007.2

    Google Scholar 

  608. Zhang, C., Zhang, Z.: A survey of recent advances in face detection. Tech. rep., Microsoft Research (2010). MSR-TR-2010-66

    Google Scholar 

  609. Zhang, G.H., Poon, C.C.Y., Zhang, Y.T.: A fast key generation method based on dynamic biometrics to secure wireless body sensor networks for p-health. In: Proceedings of 2010 International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2034–2036. IEEE, New York (2010). doi: 10.1109/IEMBS.2010.5626783

    Google Scholar 

  610. Zhang, G.H., Salganicoff, M.: Method of measuring the focus of close-up image of eyes (1999). U.S. Patent 5953440

    Google Scholar 

  611. Zhang, L., Sun, Z., Tan, T., Hu, S.: Robust biometric key extraction based on iris cryptosystem. In: Tistarelli, M., Nixon, M. (eds.) Proceedings of 3rd International Conference on Biometrics. LNCS, vol. 5558, pp. 1060–1070. Springer, New York (2009). doi: 10.1007/978-3-642-01793-3_107

    Google Scholar 

  612. Zhang, W., Chang, Y.J., Chen, T.: Optimal thresholding for key generation based on biometrics. In: Proceedings of International Conference on Image Processing, pp. 3451–3454. IEEE, New York (2004). doi: 10.1109/ICIP.2004.1421857

    Google Scholar 

  613. Zhang, X., Sun, Z., Tan, T.: Texture removal for adaptive level set based iris segmentation. In: Proceedings of 17th IEEE International Conference on Image Processing, pp. 1729–1732. IEEE, New York (2010). doi: 10.1109/ICIP.2010.5652941

    Google Scholar 

  614. Zhang, Z., Wang, R., Pan, K., Li, S., Zhang, P.: Fusion of near infrared face and iris biometrics. In: Lee, S.W., Li, S. (eds.) Proceedings of 2nd International Conference on Biometrics, LNCS, vol. 4642, pp. 172–180. Springer, New York (2007). doi: 10.1007/978-3-540-74549-5_19

    Google Scholar 

  615. Zheng, G., Li, W., Zhan, C.: Cryptographic key generation from biometric data using lattice mapping. In: Proceedings of 18th International Conference on Pattern Recognition, vol. 4, pp. 513–516. IEEE, New York (2006). doi: 10.1109/ICPR.2006.423

    Google Scholar 

  616. Zheng, Z., Yang, J., Yang, L.: A robust method for eye features extraction on color image. Pattern Recogn. Lett. 26, 2252–2261 (2005). doi: 10.1016/j.patrec.2005.03.033

    Google Scholar 

  617. Zhou, Z., Du, Y., Belcher, C.: Transforming traditional iris recognition systems to work in nonideal situations. IEEE Trans. Ind. Electron. 56(8), 3203–3213 (2009). doi: 10.1109/TIE.2009.2024653

    Google Scholar 

  618. Zhou, Z., Yingzi Du, E., Thomas, N., Delp, E.: Multi-angle sclera recognition system. In: IEEE Workshop on Computational Intelligence in Biometrics and Identity Management (CIBIM), 2011, pp. 103–108. IEEE, New York (2011). doi: 10.1109/CIBIM.2011.5949225

    Google Scholar 

  619. Ziauddin, S., Dailey, M.: Iris recognition performance enhancement using weighted majority voting. In: Proceedings of 15th International Conference on Image Processing, pp. 277–280. IEEE, New York (2008). doi: 10.1109/ICIP.2008.4711745

    Google Scholar 

  620. Zuiderveld, K.: Graphics Gems IV, chap. Contrast Limited Adaptive Histogram Equalization, pp. 474–485. Morgan Kaufmann, San Francisco (1994)

    Google Scholar 

  621. Zuo, J., Kalka, N., Schmid, N.: A robust iris segmentation procedure for unconstrained subject presentation. In: Proceedings of Biometric Consortium Conference, pp. 1–6. IEEE, New York (2006). doi: 10.1109/BCC.2006.4341623

    Google Scholar 

  622. Zuo, J., Ratha, N., Connell, J.: A new approach for iris segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshop, pp. 1–6. IEEE, New York (2008). doi: 10.1109/CVPRW.2008.4563109

    Google Scholar 

  623. Zuo, J., Ratha, N.K., Connel, J.H.: Cancelable iris biometric. In: Proceedings of 19th International Conference on Pattern Recognition pp. 1–4. IEEE, New York (2008). doi: 10.1109/ICPR.2008.4761886

    Google Scholar 

  624. Zuo, J., Schmid, N.: An automatic algorithm for evaluating the precision of iris segmentation. In: Proceedings of IEEE 2nd International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–6. IEEE, New York (2008). doi: 10.1109/BTAS.2008.4699358

    Google Scholar 

  625. Zuo, J., Schmid, N.: On a methodology for robust segmentation of nonideal iris images. IEEE Trans. Syst. Man Cybern. B Cybern. 40(3), 703–718 (2010). doi: 10.1109/TSMCB.2009.2015426

    Google Scholar 

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© 2012 Springer Science+Business Media, LLC

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Rathgeb, C., Uhl, A., Wild, P. (2012). Advances, Applications, and Challenges. In: Iris Biometrics. Advances in Information Security, vol 59. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5571-4_15

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