Fingerprint Anti-spoofing in Biometric Systems

  • Javier GalballyEmail author
  • Julian Fierrez
  • Javier Ortega-Garcia
  • Raffaele Cappelli
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)


This chapter is focused on giving a comprehensive description of the state-of-the-art in biometric-based fingerprint anti-spoofing and the big advances that have been reached in this field over the last decade. In addition, after a comprehensive review of the available literature in the field, we explore the potential of quality assessment as a way to enhance the security of the fingerprint-based technology against direct attacks. We believe that, beyond the interest that the described techniques intrinsically have, the case study presented may serve as an example of how to develop and validate fingerprint anti-spoofing techniques based on common and publicly available benchmarks and following a systematic and replicable protocol.


Image Quality Assessment Biometric System Fingerprint Image Structural Similarity Index Measure Latent Fingerprint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work has been partially supported by projects Contexts (S2009/TIC-1485) from CAM, Bio-Shield (TEC2012-34881) from Spanish MECD, TABULA RASA (FP7-ICT-257289) and BEAT (FP7-SEC-284989) from EU, and Cátedra UAM-Telefónica.


  1. 1.
    van der Putte T, Keuning J (2000) Biometrical fingerprint recognition: don’t get your fingers burned. In: Proceedings of IFIP conference on smart card research and advanced applications, pp 289–303Google Scholar
  2. 2.
    Matsumoto T, Matsumoto H, Yamada K, Hoshino S (2002) Impact of artificial gummy fingers on fingerprint systems. In: Proceedings of SPIE optical security and counterfeit deterrence techniques IV, vol 4677, pp 275–289Google Scholar
  3. 3.
    Thalheim L, Krissler J (2002) Body check: biometric access protection devices and their programs put to the test. c’t magazine, pp 114–121Google Scholar
  4. 4.
    Derakhshani R, Schuckers S, Hornak L, O’Gorman L (2003) Determination of vitality from non-invasive biomedical measurement for use in fingerprint scanners. Pattern Recogn 36: 383–396Google Scholar
  5. 5.
    Antonelli A, Capelli R, Maio D, Maltoni D (2006) Fake finger detection by skin distortion analysis. IEEE Trans Inf Forensics Secur 1:360–373CrossRefGoogle Scholar
  6. 6.
    Galbally J, Alonso-Fernandez F, Fierrez J, Ortega-Garcia J (2012) A high performance fingerprint liveness detection method based on quality related features. Future Gener Comput Syst 28:311–321CrossRefGoogle Scholar
  7. 7.
    Franco A, Maltoni D (2008) Advances in biometrics: sensors, algorithms and systems, chap. fingerprint synthesis and spoof detection. Springer, London, pp 385–406CrossRefGoogle Scholar
  8. 8.
    Li SZ (ed) (2009) Encyclopedia of biometrics. Springer, BerlinGoogle Scholar
  9. 9.
    Coli P (2008) Vitality detection in personal authentication systems using fingerprints. Ph.D. thesis, Universita di CagliariGoogle Scholar
  10. 10.
    Sandstrom M (2004) Liveness detection in fingerprint recognition systems. Master’s thesis, Linkoping UniversityGoogle Scholar
  11. 11.
    Lane M, Lordan L (2005) Practical techniques for defeating biometric devices. Master’s thesis, Dublin City UniversityGoogle Scholar
  12. 12.
    Blomme J (2003) Evaluation of biometric security systems against artificial fingers. Master’s thesis, Linkoping UniversityGoogle Scholar
  13. 13.
    Lapsley P, Less J, Pare D, Hoffman N (1998) Anti-fraud biometric sensor that accurately detects blood flow , SmartTouch, LLC, US Patent #5,737,439Google Scholar
  14. 14.
    Setlak DR (1999) Fingerprint sensor having spoof reduction features and related methods, US Patent #5,953,441Google Scholar
  15. 15.
    Kallo I, Kiss A, Podmaniczky JT (2001) Detector for recognizing the living character of a finger in a fingerprint recognizing apparatus, Dermo Corporation, Ltd. U.S. Patent #6,175,64, Jan 16 2001Google Scholar
  16. 16.
    Diaz-Santana E, Parziale G (2006) Liveness detection method. Patent pending. EP06013258, 6Google Scholar
  17. 17.
    Kim J, Choi H, Lee W (2011) Spoof detection method for touchless fingerprint acquisition apparatus, Korea Patent, 0484664Google Scholar
  18. 18.
    Centro Criptologico Nacional (CCN) (2011) Characterizing attacks to fingerprint verification mechanisms CAFVM v3.0. Common Criteria PortalGoogle Scholar
  19. 19.
    Bundesamt fur Sicherheit in der Informationstechnik (BSI) (2008) Fingerprint spoof detection protection profile FSDPP v1.8. Common Criteria PortalGoogle Scholar
  20. 20.
    Marcialis GL, Lewicke A, Tan B, Coli P, Grimberg D, Congiu A, Tidu A, Roli F, Schuckers S (2009) First international fingerprint liveness detection competition—livdet 2009. In: Proceedings of IAPR international conference on image analysis and processing (ICIAP), LNCS, vol 5716. pp 12–23Google Scholar
  21. 21.
    Yambay D, Ghiani L, Denti P, Marcialis GL, Roli F, Schuckers S (2012) LivDet2011—fingerprint liveness detection competition 2011. In: 5th International Conference Biometrics (ICB)Google Scholar
  22. 22.
    Galbally J, Fierrez J, Alonso-Fernandez F, Martinez-Diaz M (2011) Evaluation of direct attacks to fingerprint verification systems. J Telecommun Syst, Spec Issue Biometrics Syst Appl 47:243–254CrossRefGoogle Scholar
  23. 23.
    Abhyankar A, Schuckers S (2009) Integrating a wavelet based perspiration liveness check with fingerprint recognition. Pattern Recogn 42:452–464CrossRefzbMATHGoogle Scholar
  24. 24.
    Biometrics Institute (2011) Biometric Vulnerability Assessment Expert Group.
  25. 25.
    NPL (2010) National Physical Laboratory: biometrics.
  26. 26.
    CESG (2001) Communications-Electronics Security Group—Biometric Working Group (BWG).
  27. 27.
    BEAT (2012) BEAT: biometrices evaluation and testing.
  28. 28.
    Tabula Rasa (2010) Trusted biometrics under spoofing attacks (tabula rasa). (
  29. 29.
    Maltoni D, Maio D, Jain A, Prabhakar S (2009) Handbook of fingerprint recognition. Springer, LondonCrossRefGoogle Scholar
  30. 30.
    Cappelli R, Maio D, Lumini A, Maltoni D (2007) Fingerprint image reconstruction from standard templates. IEEE Trans Pattern Anal Mach Intell 29:1489–1503CrossRefGoogle Scholar
  31. 31.
    Ross A, Shah J, Jain AK (2007) From template to image: reconstructing fingerprints from minutiae points. IEEE Trans Pattern Anal Mach Intell 29:544–560CrossRefGoogle Scholar
  32. 32.
    Cappelli R (2009) Handbook of fingerprint recognition, chap. synthetic fingerprint generation. Springer, London, pp 270–302Google Scholar
  33. 33.
    Fen J, Jain A (2011) Fingerprint reconstruction: from minutiae to phase. IEEE Trans Pattern Anal Mach Intell 33:209–223CrossRefGoogle Scholar
  34. 34.
    Wehde A, Beffel JN (1924) Fingerprints can be forged. Tremonia Publish Co, ChicagoGoogle Scholar
  35. 35.
    de Water MV (1936) Can fingerprints be forged? Sci News Lett 29:90–92CrossRefGoogle Scholar
  36. 36.
    Sengottuvelan P, Wahi A (2007) Analysis of living and dead finger impressions identification for biometric applications. In: Proceedings of international conference on computational intelligence and multimedia applicationsGoogle Scholar
  37. 37.
    Yoon S, Feng J, Jain AK (2012) Altered fingerprints: analysis and detection. IEEE Trans Pattern Anal Mach Intell 34:451–464CrossRefGoogle Scholar
  38. 38.
    Willis D, Lee M (1998) Biometrics under our thumb. Network computing. Available on line at
  39. 39.
    Sten A, Kaseva A, Virtanen T (2003) Fooling fingerprint scanners—biometric vulnerabilities of the precise biometrics 100 SC scanner. In: Proceedings of australian information warfare and it security conferenceGoogle Scholar
  40. 40.
    Wiehe A, Søndrol T, Olsen OK, Skarderud F (2004) Attacking fingerprint sensors. Gjøvik University College, 200Google Scholar
  41. 41.
    Galbally J, Cappelli R, Lumini A, de Rivera GG, Maltoni D, Fierrez J, Ortega-Garcia J, Maio D (2010) An evaluation of direct and indirect attacks using fake fingers generated from ISO templates. Pattern Recogn Lett 31:725–732 (To appear)Google Scholar
  42. 42.
    Barral C, Tria A (2009) Fake fingers in fingerprint recognition: glycerin supersedes gelatin. Formal to practical security, LNCS, vol 5458. Springer, Berlin, pp 57–69Google Scholar
  43. 43.
    Parthasaradhi S, Derakhshani R, Hornak L, Schuckers S (2005) Time-series detection of perspiration as a liveness test in fingerprint devices. IEEE Trans Syst Man Cybern Part C Appl Rev 35:335–343CrossRefGoogle Scholar
  44. 44.
    Schuckers S, Abhyankar A (2004) A wavelet based approach to detecting liveness in fingerprint scanners. In: Proceedings of biometric authentication workshop (BioAW), LNCS, vol 5404. Springer, pp 278–386Google Scholar
  45. 45.
    Tan B, Schuckers S (2006) Comparison of ridge- and intensity-based perspiration liveness detection methods in fingerprint scanners. In: Proceedings of the SPIE biometric technology for human identification III (BTHI III), vol 6202, pp 62020AGoogle Scholar
  46. 46.
    Tan B, Schuckers S (2008) A new approach for liveness detection in fingerprint scanners based on valley noise analysis. J Electron Imaging 17:011009-1–011009-9CrossRefGoogle Scholar
  47. 47.
    DeCann B, Tan B, Schuckers S (2009) A novel region based liveness detection approach for fingerprint scanners. In: Proceedings of IAPR/IEEE international conference on biometrices, LNCS, vol 5558. Springer, pp 627–636Google Scholar
  48. 48.
    NexIDBiometrics (2012).
  49. 49.
    Abhyankar A, Schuckers S (2006) Fingerprint liveness detection using local ridge frequencies and multiresolution texture analysis techniques. In: Proceedings of IEEE international conference on image processing (ICIP)Google Scholar
  50. 50.
    Marasco E, Sansone C (2010) An anti-spoofing technique using multiple textural features in fingerprint scanners. In: Proceedings of IEEE workshop on biometric measurements and systems for security and medical applications (BIOMS), pp 8–14Google Scholar
  51. 51.
    Marasco E, Sansone C (2012) Combining perspiration- and morphology-based static features for fingerprint liveness detection. Pattern Recogn Lett 33:1148–1156CrossRefGoogle Scholar
  52. 52.
    Cappelli R, Maio D, Maltoni D (2001) Modelling plastic distortion in fingerprint images. In: Proceedings of international conference on pattern recognition (ICAPR), LNCS, vol 2013. Springer, pp 369–376Google Scholar
  53. 53.
    Bazen AM, Gerez SH (2003) Fingerprint matching by thin-plate spline modelling of elastic deformations. Pattern Recogn 36:1859–1867CrossRefGoogle Scholar
  54. 54.
    Chen Y, Dass S, Ross A, Jain AK (2005) Fingerprint deformation models using minutiae locations and orientations. In: Proceedings of IEEE workshop on applications of computer vision (WACV), pp 150–156Google Scholar
  55. 55.
    Chen Y, Jain AK (2005) Fingerprint deformation for spoof detection. In: Proceedings of IEEE biometric symposium (BSym), pp 19–21Google Scholar
  56. 56.
    Zhang Y, Tian J, Chen X, Yang X, Shi P (2007) Fake finger detection based on thin-plate spline distortion model. In: Proceedings of IAPR international conference on biometrics, LNCS, vol 4642. Springer, pp 742–749Google Scholar
  57. 57.
    Yau WY, Tran HT, Teoh EK, Wang JG (2007) Fake finger detection by finger color change analysis. In: Proceedings of international conference on biometrics (ICB), LNCS, vol 4642. Springer, pp 888–896Google Scholar
  58. 58.
    Jia J, Cai L (2007) Fake finger detection based on time-series fingerprint image analysis. In: Proceedings of IEEE international conference on intelligent computing (ICIC), LNCS, vol 4681. Springer, pp 1140–1150Google Scholar
  59. 59.
    Uchida K (2004) Image-based approach to fingerprint acceptability assessment. In: Proceedings of interenational conference on biometric authentication, LNCS, vol 3072. Springer, pp 194–300Google Scholar
  60. 60.
    Marcialis GL, Roli F, Tidu A (2010) Analysis of fingerprint pores for vitality detection. In: Proceedings of IEEE international conference on pattern recognition (ICPR), pp 1289–1292Google Scholar
  61. 61.
    Memon S, Manivannan N, Balachandran W (2011) Active pore detection for liveness in fingerprint identification system. In: Proceedings of IEEE telecommunications forum (TelFor), pp 619–622Google Scholar
  62. 62.
    Martinsen OG, Clausen S, Nysather JB, Grimmes S (2007) Utilizing characteristic electrical properties of the epidermal skin layers to detect fake fingers in biometric fingerprint systems-a pilot study. IEEE Trans Biomed Eng 54:891–894CrossRefGoogle Scholar
  63. 63.
    Moon YS, Chen JS, Chan KC, So K, Woo KC (2005) Wavelet based fingerprint liveness detection. Electron Lett 41(20):1112–1113CrossRefGoogle Scholar
  64. 64.
    Nikam SB, Agarwal S (2009) Feature fusion using Gabor filters and cooccrrence probabilities for fingerprint antispoofing. Int J Intell Syst Technol Appl 7:296–315Google Scholar
  65. 65.
    Nikam SB, Argawal S (2009) Ridgelet-based fake fingerprint detection. Neurocomputing 72:2491–2506CrossRefGoogle Scholar
  66. 66.
    Nikam S, Argawal S (2010) Curvelet-based fingerprint anti-spoofing. SIViP 4:75–87CrossRefGoogle Scholar
  67. 67.
    Coli P, Marcialis GL, Roli F (2007) Power spectrum-based fingerprint vitality detection. In: Proceedings of IEEE workshop on automatic identification advanced technologies (AutoID), pp 169–173Google Scholar
  68. 68.
    Jin C, Kim H, Elliott S (2007) Liveness detection of fingerprint based on band-selective Fourier spectrum. In: Proceedings of international conference on information security and cryptology (ICISC), LNCS, vol 4817. Springer, pp 168–179Google Scholar
  69. 69.
    Jin S, Bae Y, Maeng H, Lee H (2010) Fake fingerprint detection based on image analysis. In: Proceedings of SPIE 7536, sensors, cameras, and systems for industrial/scientific applications XI, pp 75360CGoogle Scholar
  70. 70.
    Lee H, Maeng H, Bae Y (2009) Fake finger detection using the fractional Fourier transform. In: Proceedings of biometric ID management and multimodal communication (BioID), LNCS, vol 5707. Springer, pp 318–324Google Scholar
  71. 71.
    Marcialis GL, Coli P, Roli F (2012) Fingerprint liveness detection based on fake finger characteristics. Int J Digit Crime Forensics 4(3):1–19CrossRefGoogle Scholar
  72. 72.
    Coli P, Marcialis GL, Roli F (2007) Vitality detection from fingerprint images: A critical survey. In: Proceedings of international conference on biometrics (ICB), LNCS, vol 4642. Springer, pp. 722–731Google Scholar
  73. 73.
    Coli P, Marcialis GL (2008) Fingerprint silicon replicas: static and dynamic features for vitality detection using an optical capture device. Int J Image Graph 8(4):495–512CrossRefGoogle Scholar
  74. 74.
    Marcialis GL, Coli P, Roli F (2012) Fingerprint liveness detection based on fake finger characteristics. Int J Digit Crime Forensics 4:1–19CrossRefGoogle Scholar
  75. 75.
    Choi H, Kang R, Choi K, Jin ATB, Kim J (2009) Fake-fingerprint detection using multiple static features. Opt Eng 48:047202-1–047202-13Google Scholar
  76. 76.
    Nixon KA, Rowe RK (2005) Multispectral fingerprint imaging for spoof detection. In: Proceedings of SPIE 5779, biometric technology for human identification II (BTHI), pp 214–225Google Scholar
  77. 77.
    Rowe RK, Nixon KA, Butler PW (2008) Multispectral fingerprint image acquisition. In: Ratha NK, Govindaraju V (eds) Advances in biometrics: sensors, algorithms and systems, Springer, London, pp 3–23Google Scholar
  78. 78.
    Yau WY, Tran HL, Teoh EK (2008) Fake finger detection using an electrotactile display system. In: Proceedings of international conference on control, automation, robotics and vision (ICARCV), pp 17–20Google Scholar
  79. 79.
    Reddy PV, Kumar A, Rahman SM, Mundra TS (2008) A new antispoofing approach for biometric devices. IEEE Trans Biomed Circuits Syst 2:328–337CrossRefGoogle Scholar
  80. 80.
    Baldiserra D, Franco A, Maio D, Maltoni D (2006) Fake fingerprint detection by odor analysis. In: Proceedings of IAPR international conference on biometrics (ICB), LNCS, vol 3832. Springer, pp 265–272Google Scholar
  81. 81.
    Cheng Y, Larin KV (2006) Artificial fingerprint recognition using optical coherence tomography with autocorrelation analysis. Appl Opt 45:9238–9245CrossRefGoogle Scholar
  82. 82.
    Manapuram RK, Ghosn M, Larin KV (2006) Identification of artificial fingerprints using optical coherence tomography technique. Asian J Phys 15:15–27Google Scholar
  83. 83.
    Cheng Y, Larin KV (2007) In vivo two- and three-dimensional imaging of artificial and real fingerprints with optical coherence tomography. IEEE Photonics Technol Lett 19:1634–1636CrossRefGoogle Scholar
  84. 84.
    Larin KV, Cheng Y (2008) Three-dimensional imaging of artificial fingerprint by optical coherence tomography. In: Proceedings of SPIE biometric technology for human identification (BTHI), vol 6944. pp 69440MGoogle Scholar
  85. 85.
    Chang S, Larin KV, Mao Y, Almuhtadi W, Flueraru C (2011) Fingerprint Spoof Detection Using Near Infrared Optical Analysis. In: Yang J, Nanni L (eds) State of the art in biometrics. Intechopen, Croatia, pp 57–84Google Scholar
  86. 86.
    Nasiri-Avanaki MR, Meadway A, Bradu A, Khoshki RM, Hojjatoleslami A, Podoleanu AG (2011) Anti-spoof reliable biometry of fingerprints using en-face optical coherence tomography. Opt Photonics J 1:91–96CrossRefGoogle Scholar
  87. 87.
    Hariri M, Shokouhi SB (2011) Possibility of spoof attack against robustness of multibiometric authentication systems. SPIE J Opt Eng 50:079001CrossRefGoogle Scholar
  88. 88.
    Akhtar Z, Fumera G, Marcialis GL, Roli F (2011) Robustness analysis of likelihood ratio score fusion rule for multi-modal biometric systems under spoof attacks. In: Proceedings of IEEE international carnahan conference on security technology (ICSST), pp 237–244Google Scholar
  89. 89.
    Marasco E, Johnson P, Sansone C, Schuckers S (2011) Increase the security of multibiometric systems by incorporating a spoofing detection algorithm in the fusion mechanism. In: Proceedings of multiple classifier systems (MCS), LNCS, vol 6713. Springer, pp 309–318Google Scholar
  90. 90.
    Marasco E, Ding Y, Ross A (2012) Combining match scores with liveness values in a fingerprint verification system. In: Proceedings of IEEE international conference on biometrics: theory, applications and systems (BTAS), pp 418–425Google Scholar
  91. 91.
    Rattani A, Poh N, Ross A (2012) Analysis of user-specific score characteristics for spoof biometric attacks. In: Proceedings of IEEE computer society workshop on biometrics at the international conference on computer vision and pattern recognition (CVPR), pp 124–129Google Scholar
  92. 92.
    Akhtar Z, Fumera G, Marcialis GL, Roli F (2012) Evaluation of serial and parallel multibiometric systems under spoofing attacks. In: Proceedings of international conference on biometrics: theory, applications and systems (BTAS), pp 402–407Google Scholar
  93. 93.
    Ultra-Scan (2012).
  94. 94.
    Optel (2012).
  95. 95.
  96. 96.
    VirdiTech (2012).
  97. 97.
    Kang H, Lee B, Kim H, Shin D, Kim J (2003) A study on performance evaluation of the liveness detection for various fingerprint sensor modules. In: Proceedings of international conference on knowledge-based intelligent information and engineering systems (KES), LNAI, vol 2774. Springer, pp 1245–1253Google Scholar
  98. 98.
    Wang L, El-Maksoud RA, Sasian JM, William Kuhn P, Gee K (2009) V.S.V.: A novel contactless aliveness-testing fingerprint sensor. In: Proceedings of SPIE novel optical systems design and optimization XII, vol 7429. p. 742915Google Scholar
  99. 99.
    Bayram S, Avcibas I, Sankur B, Memon N (2006) Image manipulation detection. J Electron Imagingq 15(041):102Google Scholar
  100. 100.
    Stamm MC, Liu KJR (2010) Forensic detection of image manipulation using statistical intrinsic fingerprints. IEEE Trans Inf Forensics Secur 5:492–496CrossRefGoogle Scholar
  101. 101.
    Avcibas I, Memon N, Sankur B (2003) Steganalysis using image quality metrics. IEEE Trans Image Process 12:221–229CrossRefMathSciNetGoogle Scholar
  102. 102.
    Avcibas I, Kharrazi M, Memon N, Sankur B (2005) Image steganalysis with binary similarity measures. EURASIP J Appl Sig Process 1:2749–2757CrossRefGoogle Scholar
  103. 103.
    Lyu S, Farid H (2006) Steganalysis using higher-order image statistics. IEEE Trans Inf Forensics Secur 1:111–119CrossRefGoogle Scholar
  104. 104.
    Lim E, Jiang X, Yau W (2002) Fingerprint quality and validity analysis. In: Proceedings of IEEE international conference on image processing (ICIP), vol 1. pp 469–472Google Scholar
  105. 105.
    Chen Y, Dass S, Jain A (2005) Fingerprint quality indices for predicting authentication performance. In: Proceedings of IAPR audio- and video-based biometric person authentication (AVBPA), LNCS, vol 3546. Springer, pp 160–170Google Scholar
  106. 106.
    Chen T, Jiang X, Yau W (2004) Fingerprint image quality analysis. In: Proceedings of IEEE international conference on image processing (ICIP), vol 2. pp 1253–1256Google Scholar
  107. 107.
    Hong L, Wan Y, Jain AK (1998) Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans Pattern Anal Mach Intell 20(8):777–789CrossRefGoogle Scholar
  108. 108.
    Alonso-Fernandez F, Fierrez J, Ortega-Garcia J, Gonzalez-Rodriguez J, Fronthaler H, Kollreider K, Bigun J (2008) A comparative study of fingerprint image quality estimation methods. IEEE Trans Inf Forensics Secur 2(4):734–743CrossRefGoogle Scholar
  109. 109.
    Bigun J (2006) Vision with direction. Springer, BerlinGoogle Scholar
  110. 110.
    Shen L, Kot A, Koo W (2001) Quality measures of fingerprint images. In: Proceedings of IAPR audio- and video-based biometric person authentication (AVBPA), LNCS, vol 2091. Springer, pp 266–271Google Scholar
  111. 111.
    Wong PW, Pappas TN, Safranek RJ, Chen J, Wang Z, Bovik AC, Simoncelli EP, Sheikh HR (2005) Handbook of image and video processing, chap. Section VIII: image and video rendering and Assessment. Academic Press, Amsterdam, pp 925–989CrossRefGoogle Scholar
  112. 112.
    Sheikh HRS, Sabir MF, Bovik AC (2006) A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans Image Process 15:3440–3451CrossRefGoogle Scholar
  113. 113.
    Saad MA, Bovik AC, Charrier C (2012) Blind image quality assessment: A natural scene statatistics approach in the DCT domain. IEEE Trans Image Process 21:3339–3352CrossRefMathSciNetGoogle Scholar
  114. 114.
    Avcibas I, Sankur B, Sayood K (2002) Statistical evaluation of image quality measures. J Electron Imaging 11:206–223CrossRefGoogle Scholar
  115. 115.
    Huynh-Thu Q, Ghanbari M (2008) Scope of validity of PSNR in image/video quality assessment. Electron Lett 44:800–801CrossRefGoogle Scholar
  116. 116.
    Yao S, Lin W, Ong E, Lu Z (2005) Contrast signal-to-noise ratio for image quality assessment. In: Proceedings of international conference on image processing (ICIP), pp 397–400Google Scholar
  117. 117.
    Eskicioglu AM, Fisher PS (1995) Image quality measures and their performance. IEEE Trans Commun 43:2959–2965CrossRefGoogle Scholar
  118. 118.
    Martini MG, Hewage CT, Villarini B (2012) Image quality assessment based on edge preservation. Sig Process: Image Commun 27:875–882Google Scholar
  119. 119.
    Nill NB, Bouzas B (1992) Objective image quality measure derived from digital image power spectra. Opt Eng 31:813–825CrossRefGoogle Scholar
  120. 120.
    Liu A, Lin W, Narwaria M (2012) Image quality assessment based on gradient similarity. IEEE Trans Image Process 21:1500–1511CrossRefMathSciNetGoogle Scholar
  121. 121.
    Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13:600–612CrossRefGoogle Scholar
  122. 122.
  123. 123.
    Sheikh HR, Bovik AC (2006) Image information and visual quality. IEEE Trans Image Process 15:430–444CrossRefGoogle Scholar
  124. 124.
    Soundararajan R, Bovik AC (2012) RRED indices: reduced reference entropic differencing for image quality assessment. IEEE Trans Image Process 21:517–526CrossRefMathSciNetGoogle Scholar
  125. 125.
    Wang Z, Sheikh HR, Bovik AC (2002) No-reference perceptual quality assessment of JPEG compressed images. In: Proceedings of IEEE international conference on image processing (ICIP), pp 477–480Google Scholar
  126. 126.
    Zhu X, Milanfar P (2009) A no-reference sharpness metric sensitive to blur and noise. In: Proceedings of international workshop on quality of multimedia experience (QoMEx), pp 64–69Google Scholar
  127. 127.
    Moorthy AK, Bovik AC (2010) A two-step framework for constructing blind image quality indices. IEEE Sig Process Lett 17:513–516CrossRefGoogle Scholar
  128. 128.
    Mittal A, Soundararajan R, Bovik AC (2012) Making a completely blind image quality analyzer. IEEE Sig Process Lett. doi: 10.1109/LSP.2012.2227726
  129. 129.
    Wang Z, Bovik AC (2009) Mean squared error: love it or leave it? IEEE Sig Process Mag 26:98–117CrossRefGoogle Scholar
  130. 130.
    Teo PC, Heeger DJ (1994) Perceptual image distortion. In: Proceedings of international conference on image processing, pp 982–986Google Scholar
  131. 131.
    Harris C, Stephens M (1988) A combined corner and edge detector. In: Proceedings of alvey vision conference (AVC), pp 147–151Google Scholar
  132. 132.
    Brunet D, Vrscay ER, Wang Z (2012) On the mathematical properties of the structural similarity index. IEEE Trans Image Process 21:1488–1499CrossRefMathSciNetGoogle Scholar
  133. 133.
    Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning. Springer, New YorkCrossRefzbMATHGoogle Scholar
  134. 134.
    Nixon KA, Aimale V, Rowe RK (2008) Handbook of biometrics, chap. spoof detection schemes. Springer, New York, pp 403–423CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  • Javier Galbally
    • 1
    Email author
  • Julian Fierrez
    • 1
  • Javier Ortega-Garcia
    • 1
  • Raffaele Cappelli
    • 1
    • 2
  1. 1.Biometric Recognition Group—ATVSUniversidad Autonoma de MadridMadridSpain
  2. 2.Biometric Systems Laboratory (BioLab)Universit‘a di BolognaBolognaItaly

Personalised recommendations