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Overview: High Resolution Fingerprints

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Advanced Fingerprint Recognition: From 3D Shape to Ridge Detail

Abstract

This chapter provides an overview of Part II with focus on the background and development of fingerprint recognition using high resolution images. We first discuss the significance of high resolution fingerprint recognition in the context of fingerprint recognition history, and then introduce fingerprint features, particularly the features available on high resolution fingerprint images. Some high resolution fingerprint recognition systems are then discussed, followed by benchmarks of high resolution fingerprint images in the literature and the recent development of deep learning based fingerprint recognition methods. Finally, a brief summary of the chapters in Part II is given.

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References

  1. Anand, V., Kanhangad, V.: Pore detection in high-resolution fingerprint images using deep residual network. J. Electron. Imaging. 28(2), 1–4 (2019)

    Article  Google Scholar 

  2. Ashbaugh, D.R.: Quantitative-Qualitative Friction Ridge Analysis: An Introduction to Basic and Advanced Ridgeology. CRC Press LLC, Boca Raton (1999)

    Book  Google Scholar 

  3. Bindra, B., Jasuja, O.P., Singla, A.K.: Poroscopy: a method of personal identification revisited. Internet J. Forensic Med. Toxicol. 1, (2000)

    Google Scholar 

  4. Cao, K., Jain, A.K.: Automated latent fingerprint recognition. IEEE Trans. Pattern Anal. Mach. Intell. 41(4), 788–800 (2019)

    Article  Google Scholar 

  5. Cappelli, R., Maio, D., Maltoni, D.: Fingerprint classification by directional image partitioning. IEEE Trans. Pattern Anal. Mach. Intell. 21(5), 402–421 (1999)

    Article  Google Scholar 

  6. CDEFFS: Data Format for the Interchange of Extended Fingerprint and Palmprint Features. Draft Version 0.4, available at http://fingerprint.nist.gov/standard/cdeffs/index.html (2009)

  7. Chen, J., Moon, Y.S.: The statistical modeling of fingerprint minutiae distribution with implications for fingerprint individuality studies. In: Proceedings of 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7 (2008)

    Google Scholar 

  8. Chen, X., Tian, J., Yang, X., Zhang, Y.: An algorithm for distorted fingerprint matching based on local triangle feature set. IEEE Trans. Inf. Forensics Secur. 1, 169–177 (2006)

    Article  Google Scholar 

  9. Chen, Y., Dass, S., Jain, A.K.: Fingerprint quality indices for predicting authentication performance. In: Proceedings of Audio- and Video-based Biometric Person Authentication, pp. 160–170 (2005)

    Google Scholar 

  10. Chen, Y., Jain, A.K.: Dots and Incipients: extended Features for Partial Fingerprint Matching. Presented at Biometric Symposium, BCC, Baltimore (2007)

    Google Scholar 

  11. Chen, Y., Jain, A. K.: Beyond minutiae: A fingerprint individuality model with pattern, ridge and pore features. In: Proceedings of the 3rd International Conference on Biometrics, pp. 523–533 (2009)

    Chapter  Google Scholar 

  12. Chugh, T., Cao, K., Zhou, J., Tabassi, E., Jain, A.K.: Latent fingerprint value prediction: crowd-based learning. IEEE Trans. Inf. Forensics Secur. 13(1), 20–34 (2018)

    Article  Google Scholar 

  13. Dai, X., Liang, J., Zhao, Q., Liu, F.: Fingerprint segmentation via convolutional neural networks. In: Proceedings of Chinese Conference on Biometric Recognition, pp. 324–333 (2017)

    Chapter  Google Scholar 

  14. FBI (Federal Bureau of Investigation): The Science of Fingerprints: Classification and Uses. U.S. Government Printing Office, Washington, DC (1984)

    Google Scholar 

  15. Feng, J.: Combining minutiae descriptors for fingerprint matching. Pattern Recogn. 41(1), 342–352 (2008)

    Article  MATH  Google Scholar 

  16. Feng, J., Ouyang, Z., Cai, A.: Fingerprint matching using ridges. Pattern Recogn. 39(11), 2131–2140 (2006)

    Article  MATH  Google Scholar 

  17. Hong, L., Wan, Y., Jain, A.K.: Fingerprint image enhancement: algorithms and performance evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 20, 777–789 (1998)

    Article  Google Scholar 

  18. IBG (International Biometric Group): Analysis of level 3 features at high resolutions. Phase II – Final Report (2008)

    Google Scholar 

  19. Jain, A.K., Arora, S.S., Cao, K., Best-Rowden, L., Bhatnagar, A.: Fingerprint recognition of young children. IEEE Trans. Inf. Forensics Secur. 12(7), 1501–1514 (2017)

    Article  Google Scholar 

  20. Jain, A.K., Chen, Y., Demirkus, M.: Pores and ridges: fingerprint matching using level 3 features. IEEE Trans. Pattern Anal. Mach. Intell. 29(1), 15–27 (2007)

    Article  Google Scholar 

  21. Jain, A.K., Feng, J.: Latent fingerprint matching. IEEE Trans. Pattern Anal. Mach. Intell. 33(1), 88–100 (2011)

    Article  Google Scholar 

  22. Jain, A.K., Hong, L., Bolle, R.: On-line fingerprint verification. IEEE Trans. Pattern Anal. Mach. Intell. 19, 302–314 (1997)

    Article  Google Scholar 

  23. Jain, A.K., Prabhakar, S., Hong, L., Pankanti, S.: Filterbank-based fingerprint matching. IEEE Trans. Image Process. 9, 846–859 (2000)

    Article  Google Scholar 

  24. Jea, T.Y., Govindaraju, V.: A minutia-based partial fingerprint recognition system. Pattern Recognition 38, 1672–1684 (2005)

    Article  Google Scholar 

  25. Kovacs-Vajna, Z.M.: A fingerprint verification system based on triangular matching and dynamic time warping. IEEE Trans. Pattern Anal. Mach. Intell. 22, 1266–1276 (2000)

    Article  Google Scholar 

  26. Kryszczuk, K., Drygajlo, A., Morier, P.: Extraction of level 2 and level 3 features for fragmentary fingerprints. In: Proceedings of the 2nd COST Action 275 Workshop, pp. 83–88. Vigo, Spain (2004)

    Google Scholar 

  27. Kryszczuk, K., Morier, P., Drygajlo, A.: Study of the distinctiveness of level 2 and level 3 features in fragmentary fingerprint comparison. In: Proceedings of Biometric Authentication, ECCV 2004 International Workshop, pp. 124–133 (2004)

    Chapter  Google Scholar 

  28. Liu, E., Cao, K.: Minutiae extraction from level 1 features of fingerprint. IEEE Trans. Inf. Forensics Secur. 11(9), 1893–1902 (2016)

    Article  Google Scholar 

  29. Liu, L., Jiang, T., Yang, J., Zhu, C.: Fingerprint registration by maximization of mutual information. IEEE Trans. Image Process. 15, 1100–1110 (2006)

    Article  Google Scholar 

  30. Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition, 2nd edn. Springer, New York (2009)

    Book  MATH  Google Scholar 

  31. Mehtre, B., Murthy, M.: A minutiae based fingerprint identification system. In: Proceedings of the 2nd International Conference on Advances in Pattern Recognition and Digital Techniques (1986)

    Google Scholar 

  32. NIST Special Database 30, available at http://www.nist.gov/srd/nistsd30.htm

  33. Pankanti, S., Prabhakar, S., Jain, A.K.: On the individuality of fingerprints. IEEE Trans. Pattern Anal. Mach. Intell. 24(8), 1010–1025 (2002)

    Article  MATH  Google Scholar 

  34. Parsons, N.R., Smith, J.Q., Thonnes, E., Wang, L., Wilson, R.G.: Rotationally invariant statistics for examining the evidence from the pores in fingerprints. Law Probab. Risk. 7, 1–14 (2008)

    Article  Google Scholar 

  35. Pernus, F., Kovacic, S., Gyergyek, L.: Minutiae-based fingerprint recognition. In: Proceedings of the 5th International Conference on Pattern Recognition, pp. 1380–1382 (1980)

    Google Scholar 

  36. The PolyU High Resolution Fingerprint Database. http://www4.comp.polyu.edu.hk/~biometrics/HRF/HRF_old.htm (2008)

  37. Ratha, N., Bolle, R.: Automatic Fingerprint Recognition Systems. Springer, New York (2004)

    Book  Google Scholar 

  38. Ratha, N.K., Connell, J.H., Bolle, R.M.: Image mosaicing for rolled fingerprint construction. In: Proceedings of the 14th International Conference on Pattern Recognition, vol. 2, pp. 1651–1653 (1998)

    Google Scholar 

  39. Roddy, A., Stosz, J.: Fingerprint features – statistical analysis and system performance estimates. Proc. IEEE. 85(9), 1390–1421 (1997)

    Article  Google Scholar 

  40. Ross, A., Dass, S., Jain, A.K.: A deformable model for fingerprint matching. Pattern Recogn. 38, 95–103 (2005)

    Article  Google Scholar 

  41. Ross, A., Jain, A.K., Reisman, J.: A hybrid fingerprint matcher. Pattern Recogn. 36, 1661–1673 (2003)

    Article  Google Scholar 

  42. Sherlock, B.G., Monro, D.M., Millard, K.: Fingerprint enhancement by directional Fourier filtering. IEE Proc. Vision Image Signal Process. 141(2), 87–94 (1994)

    Article  Google Scholar 

  43. Song, D., Feng, J.: Fingerprint indexing based on pyramid deep convolutional feature. In: Proceedings of International Joint Conference on Biometrics, pp. 200–207 (2017)

    Google Scholar 

  44. Song, D, Tang, Y, Feng, J.: Fingerprint indexing based on minutia-centred deep convolutional features. In: Proceedings of Asian Conference on Pattern Recognition, pp. 770–775 (2017)

    Google Scholar 

  45. Song, D., Tang, Y., Feng, J.: Aggregating minutia-centred deep convolutional features for fingerprint indexing. Pattern Recogn. 88, 397–408 (2019)

    Article  Google Scholar 

  46. Stosz, J.D., Alyea, L.A.: Automated system for fingerprint authentication using pores and ridge structure. In: Proceedings of SPIE Conference on Automatic Systems for the Identification and Inspection of Humans, San Diego, vol. 2277, pp. 210–223 (1994)

    Google Scholar 

  47. Tang, Y., Gao, F., Feng, J.: Latent fingerprint minutia extraction using fully convolutional network. In: Proceedings of International Joint Conference on Biometrics, pp. 117–123 (2017)

    Google Scholar 

  48. Tang, Y., Gao, F., Feng, J, Liu, Y.: FingerNet: an unified deep network for fingerprint minutiae extraction. In: Proceedings of International Joint Conference on Biometrics, pp. 108–116 (2017)

    Google Scholar 

  49. Teixeira, R.F.S., Leite, N.J.: Improving pore extraction in high resolution fingerprint images using spatial analysis. In: Proceedings of IEEE International Conference on Image Processing, pp. 4962–4966 (2014)

    Google Scholar 

  50. Teixeira, R.F.S., Leite, N.J.: A new framework for quality assessment of high-resolution fingerprint images. IEEE Trans. Pattern Anal. Mach. Intell. 39(10), 1905–1917 (2017)

    Article  Google Scholar 

  51. Trauring, M.: Automatic comparison of finger-ridge patterns. Nature. 197, 938–940 (1963)

    Article  Google Scholar 

  52. Wang, C., Li, K., Wu, Z., Zhao, Q.: A DCNN based fingerprint liveness detection algorithm with voting strategy. In: Proceedings of Chinese Conference on Biometric Recognition, pp. 241–249 (2015)

    Chapter  Google Scholar 

  53. Xu, Y., Lu, G., Lu, Y., Zhang, D.: High resolution fingerprint recognition using pore and edge descriptors. Pattern Recogn. Lett. 125, 773–779 (2019)

    Article  Google Scholar 

  54. Yan, J., Dai, X., Zhao, Q., Liu, F.: A CNN-based fingerprint image quality assessment method. In: Proceedings of Chinese Conference on Biometric Recognition, pp. 344–352 (2017)

    Chapter  Google Scholar 

  55. Yang, X., Feng, J., Zhou, J.: Localized dictionaries based orientation field estimation for latent fingerprints. IEEE Trans. Pattern Anal. Mach. Intell. 36(5), 955–969 (2014)

    Article  Google Scholar 

  56. Zhang, D., Liu, F., Zhao, Q., Lu, G., Luo, N.: Selecting a reference high resolution for fingerprint recognition using minutiae and pores. IEEE Trans. Instrum. Meas. 60(3), 863–871 (2011)

    Article  Google Scholar 

  57. Zhang, F., Feng, J.: High-resolution mobile fingerprint matching via deep joint KNN-triplet embedding. In: Proceedings of AAAI Conference on Artificial Intelligence, pp. 5019–5020 (2017)

    Google Scholar 

  58. Zhang, F., Xin, S., Feng, J.: Deep dense multi-level feature for partial high-resolution fingerprint matching. In: Proceedings of International Joint Conference on Biometrics, pp. 397–405 (2017)

    Google Scholar 

  59. Zhang, F., Xin, S., Feng, J.: Combining global and minutia deep features for partial high-resolution fingerprint matching. Pattern Recogn. Lett. 119, 139–147 (2019)

    Article  Google Scholar 

  60. Zhao, Q., Jain, A.K.: On the utility of extended fingerprint features: a study on pores. In: Proceedings of CVPR Workshop on Biometrics, pp. 9–16 (2010)

    Google Scholar 

  61. Zhao, Q., Jain, A.K.: Model based separation of overlapping latent fingerprints. IEEE Trans. Inf. Forensics Secur. 7(3), 904–918 (2012)

    Article  Google Scholar 

  62. Zhao, Q., Zhang, D., Zhang, L., Luo, N.: Adaptive fingerprint pore modeling and extraction. Pattern Recogn. 43(8), 2833–2844 (2010)

    Article  MATH  Google Scholar 

  63. Zhao, Q., Zhang, D., Zhang, L., Luo, N.: High resolution partial fingerprint alignment using pore-valley descriptors. Pattern Recogn. 43(3), 1050–1061 (2010)

    Article  MATH  Google Scholar 

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Liu, F., Zhao, Q., Zhang, D. (2020). Overview: High Resolution Fingerprints. In: Advanced Fingerprint Recognition: From 3D Shape to Ridge Detail. Springer, Singapore. https://doi.org/10.1007/978-981-15-4128-5_6

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  • DOI: https://doi.org/10.1007/978-981-15-4128-5_6

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