Advertisement

Robustness of Biometrics by Image Processing Technology

  • Robin FayEmail author
  • Christoph Ruland
Chapter

Abstract

Feature extraction is the most critical part of biometric authentication systems. The majority of biometric systems proposed in the last years are using alignment to ensure robust authentication in the presence of affine transformations like rotation and translation. Nevertheless, alignment is time consuming, and misalignment leads to the lack of accuracy. Using template-protection, there is a need for additional information to perform explicit alignment. It is therefore not clear whether this information could be used to attack the protected biometric template. This Chapter presents a comparative view on alignment-free features for biometric authentication from the perspective of pattern recognition and digital image processing as well as biometrics. The basics of these disciplines are aggregated and different proposed techniques are described, assessed and compared. Finally, an evaluation strategy from the field of digital image processing is applied to biometrics in order to assess robustness and invariance of feature extraction in biometrics.

Keywords

Feature Extraction Method Equal Error Rate Biometric System Invariant Moment Biometric Template 
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.

References

  1. 1.
    Wood J. Invariant pattern recognition: a review. Pattern Recognit. 1996;29(1):1–17.Google Scholar
  2. 2.
    Steger C, Ulrich M, Weidemann C. Machine vision algorithms and applications. Weinheim: Wiley-VCH 2008.Google Scholar
  3. 3.
    Theodoridis S, Koutroumbas K. Pattern recognition. 4th ed. Amsterdam: Elsevier Academic Press; 2008.Google Scholar
  4. 4.
    Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res. 2003;3:1157–82.Google Scholar
  5. 5.
    Nixon M, Aguado AS. Feature extraction & image processing. Amsterdam: Elsevier Academic Press; 2008.Google Scholar
  6. 6.
    Bishop CM. Neural networks for pattern recognition. Oxford University Press; 1995.Google Scholar
  7. 7.
    Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273–97.Google Scholar
  8. 8.
    Bishop CM, et al. Pattern recognition and machine learning. vol. 1. New York: Springer; 2006.Google Scholar
  9. 9.
    Prokop RJ, Reeves AP. A survey of moment-based techniques for unoccluded object representation and recognition. CVGIP: Graph Models Image Process. 1992;54(5):438–60.Google Scholar
  10. 10.
    Teague MR. Image analysis via the general theory of moments*. JOSA. 1980;70(8):920–30.Google Scholar
  11. 11.
    Mercimek M, Gulez K, Mumcu TV. Real object recognition using moment invariants. Sadhana. 2005;30(6):765–75.Google Scholar
  12. 12.
    Hu MK. Visual pattern recognition by moment invariants. IRE Trans Inf Theory. 1962;8(2):179–87.Google Scholar
  13. 13.
    Yang J. Biometrics. Non-minutiae based fingerprint descriptor, chapter 4. InTech, June 2011.Google Scholar
  14. 14.
    Teh C-H, Chin RT. On image analysis by the methods of moments. IEEE Trans Pattern Anal Mach Intell. 1988;10(4):496–513.Google Scholar
  15. 15.
    Khotanzad A, Hong YH. Invariant image recognition by zernike moments. IEEE Trans Pattern Anal Mach Intell. 1990;12(5):489–97.Google Scholar
  16. 16.
    Schmid C, Mohr R. Local grayvalue invariants for image retrieval. IEEE Trans Pattern Anal Mach Intell. 1997;19(5):530–5.Google Scholar
  17. 17.
    Tuytelaars T, Mikolajczyk K. Local invariant feature detectors: a survey. Found Trend® Comput Graph Vis. 2008;3(3):177–280.Google Scholar
  18. 18.
    Mikolajczyk K, Schmid C. Scale & affine invariant interest point detectors. Int J Comput Vis. 2004;60(1):63–86.Google Scholar
  19. 19.
    Mikolajczyk K, Schmid C. A performance evaluation of local descriptors. IEEE Trans Pattern Anal Mach Intell. 2005;27(10):1615–30.Google Scholar
  20. 20.
    Jain AK, Ross A, Prabhakar S. An introduction to biometric recognition. IEEE Trans Circuit Syst Video Technol. 2004;14(1):4–20.Google Scholar
  21. 21.
    Maltoni D, Maio D, Jain AK, Prabhakar S. Handbook of fingerprint recognition. London: Springer; 2009.Google Scholar
  22. 22.
    Jain AK, Flynn P, Ross AA. Handbook of biometrics. London: Springer; 2010.Google Scholar
  23. 23.
    Bundesamt für Sicherheit in der Informationstechnik (BSI). BioKeyS Pilot-DB Teil 2 (Projekt Template Protection), Abschlussbericht. 2011. https://www.bsi.bund.de/SharedDocs/Downloads/DE/BSI/Publikationen/Studien/BioKeys/BioKeyS-Abschlussbericht.pdf?__blob=publicationFile. Accessed 15 April 2014.
  24. 24.
    International Organization for Standardization and International Electrotechnical Commission. ISO/IEC 19794.Google Scholar
  25. 25.
    Lee C, Choi JY, Toh KA, Lee S. Alignment-free cancelable fingerprint templates based on local minutiae information. IEEE Trans Syst Man Cybern Part B: Cybern. 2007;37(4):980–92.Google Scholar
  26. 26.
    Rathgeb C, Uhl A. A survey on biometric cryptosystems and cancelable biometrics. EURASIP J Inf Secur. 2011;2011(1):1–25.Google Scholar
  27. 27.
    Juels A, Sudan M. A fuzzy vault scheme. Des Code Cryptogr. 2006;38(2):237–57.Google Scholar
  28. 28.
    Bundesamt für Sicherheit in der Informationstechnik (BSI). Fingerabdruckerkennung. 2013. URL. Accessed 28 Nov 2013.Google Scholar
  29. 29.
    Henry ER. Classification and uses of finger prints. Routledge; 1900.Google Scholar
  30. 30.
    ANSI/NIST. ANSI/NIST-ITL-1-2011. 2011. URL.Accessed 2 Dec 2013.Google Scholar
  31. 31.
    Jain AK, Chen Y, Demirkus M. Pores and ridges: high-resolution fingerprint matching using level 3 features. IEEE Trans Pattern Anal Mach Intell. 2007;29(1):15–27.Google Scholar
  32. 32.
    Park U, Pankanti S, Jain AK. Fingerprint verification using sift features. In: SPIE. 2008; vol. 6944, pp. 69440K.Google Scholar
  33. 33.
    Pang S, Yin Y, Yang G, Li Y. Rotation invariant finger vein recognition. In: Biometric recognition, pp. 151–6. Springer; 2012.Google Scholar
  34. 34.
    He S, Zhang C, Hao P. Comparative study of features for fingerprint indexing. In: 16th IEEE International Conference on Image Processing (ICIP). 2009; pp. 2749–52.Google Scholar
  35. 35.
    Fay R. An analysis of alignment-free feature-extraction methods for fingerprint and vein biometrics. Master’s thesis, University of Siegen; 2014.Google Scholar
  36. 36.
    Miura N, Nagasaka A, Miyatake T. Extraction of finger-vein patterns using maximum curvature points in image profiles. IEICE Trans Inf Syst. 2007;90(8):1185–94.Google Scholar
  37. 37.
    Miura N, Nagasaka A, Miyatake T. Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification. Mach Vis Appl. 2004;15(4):194–203.Google Scholar
  38. 38.
    Miura BT, et al. Vein extraction methods. 2012. http://www.mathworks.com/matlabcentral/fileexchange/35716-miura-et-al-vein-extraction-methods, Accessed 6 Dec 2013.
  39. 39.
    Hartung D. Vascular pattern recognition: and its application in privacy-preserving biometric online-banking systems. PhD thesis, Gjøvik University College; 2012.Google Scholar
  40. 40.
    Xueyan L, Shuxu G. The fourth biometric-vein recognition, pattern recognition techniques, technology and applications. InTech, 2008.Google Scholar
  41. 41.
    Xueyan L, Shuxu G, Fengli G, Ye L. Vein pattern recognitions by moment invariants. In the 1st International Conference on Bioinformatics and Biomedical Engineering, ICBBE. 2007; pp. 612–5.Google Scholar
  42. 42.
    Hartung D. Venenbilderkennung. Datenschutz und Datensicherheit—DuD. 2009;33(5):275–9.Google Scholar
  43. 43.
    Hartung D, Tistarelli M, Busch C. Vein minutia cylinder-codes (v-mcc). In International Conference on Biometrics (ICB). 2013; pp. 1–7.Google Scholar
  44. 44.
    Hartung D, Olsen MA, Xu H, Busch C. Spectral minutiae for vein pattern recognition. In International Joint Conference on Biometrics (IJCB). 2011; pp. 1–7.Google Scholar
  45. 45.
    Bansal R, Sehgal P, Bedi P. Minutiae extraction from fingerprint images-a review. arXiv preprint arXiv:1201.1422, 2011.Google Scholar
  46. 46.
    Maio D, Maltoni D. Direct gray-scale minutiae detection in fingerprints. IEEE Trans Pattern Anal Mach Intell. 1997;19(1):27–40.Google Scholar
  47. 47.
    Athi. Fingerprint Minutiae Extraction: 2011. http://www.mathworks.com/matlabcentral/fileexchange/31926-fingerprint-minutiae-extraction. Accessed 3 Jan 2014.
  48. 48.
    Sagar VK, Alex KJB. Hybrid fuzzy logic and neural network model for fingerprint minutiae extraction. In International Joint Conference on Neural Networks, IJCNN. 1999; vol. 5, pp. 3255–9.Google Scholar
  49. 49.
    Schmid C, Mohr R, Bauckhage C. Evaluation of interest point detectors. Int J Comput Vis. 2000;37(2):151–72.Google Scholar
  50. 50.
    Harris C, Stephens M. A combined corner and edge detector. In: Alvey vision conference. Manchester, UK. 1988; vol. 15, pp. 50.Google Scholar
  51. 51.
    Rosten E, Porter R, Drummond T. Faster and better: a machine learning approach to corner detection. IEEE Trans Pattern Anal Mach Intell. 2010;32(1):105–19.Google Scholar
  52. 52.
    Mair E, Hager GD, Burschka D, Suppa M, Hirzinger G. Adaptive and generic corner detection based on the accelerated segment test. In Proceedings of the European Conference on Computer Vision (ECCV'10), September 2010.Google Scholar
  53. 53.
    Rublee E, Rabaud V, Konolige K, Bradski G. Orb: an efficient alternative to sift or surf. In IEEE International Conference on Computer Vision (ICCV). 2011; pp. 2564–71.Google Scholar
  54. 54.
    Li J, Allinson NM. A comprehensive review of current local features for computer vision. Neurocomputing. 2008;71(10):1771–87.Google Scholar
  55. 55.
    Lowe DG. Object recognition from local scale-invariant features. In: The proceedings of the seventh IEEE International Conference on Computer vision. 1999; vol. 2, pp. 1150–7.Google Scholar
  56. 56.
    Lowe DG. Distinctive image features from scale-invariant keypoints. Int J Comput Vis. 2004;60(2):91–110.Google Scholar
  57. 57.
    Lindeberg T. Scale-space theory in computer vision. Dordrecht: Springer; 1993.Google Scholar
  58. 58.
    Bay H, Tuytelaars T, Van Gool L. Surf: speeded up robust features. In Computer Vision–ECCV 2006, pp. 404–17. Springer; 2006.Google Scholar
  59. 59.
    Matas J, Chum O, Urban M, Pajdla T. Robust wide-baseline stereo from maximally stable extremal regions. Image Vis Comput. 2004;22(10):761–7.Google Scholar
  60. 60.
    Dalal N, Triggs B. Histograms of oriented gradients for human detection. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2005; vol. 1, pp. 886–93.Google Scholar
  61. 61.
    Jin L, Zhang TX. The generalization of moment invariants. Chin J Comput. 2004;5:011.Google Scholar
  62. 62.
    Deepika CL, Kandaswamy A, Vimal C, Sathish B. Invariant feature extraction from fingerprint biometric using pseudo Zernike moments. In Proceedings of the International Joint Journal Conference on Engineering and Technology. 2010; pp. 104–8.Google Scholar
  63. 63.
    Yang JC, Park DS. Fingerprint verification based on invariant moment features and nonlinear BPNN. Int J Control Autom Syst. 2008;6(6):800–8.Google Scholar
  64. 64.
    Chikkerur S, Cartwright AN, Govindaraju V. Fingerprint enhancement using STFT analysis. Pattern Recognit. 2007;40(1):198–211.Google Scholar
  65. 65.
    Hong L, Wan Y, Jain A. Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans Pattern Anal Mach Intell. 1998;20(8):777–89.Google Scholar
  66. 66.
    Maio D, Maltoni D, Cappelli R, Wayman JL, Jain AK. Fvc2002: second fingerprint verification competition. In Proceedings 16th International Conference on Pattern Recognition. 2002; vol. 3, pp. 811–4.Google Scholar
  67. 67.
    Jain A, Nandakumar K, Ross A. Score normalization in multimodal biometric systems. Pattern recognition. 2005;38(12):2270–85.Google Scholar
  68. 68.
    Shuai X, Zhang C, Hao P. Fingerprint indexing based on composite set of reduced sift features. In 19th International Conference on Pattern Recognition. ICPR 2008; pp. 1–4.Google Scholar
  69. 69.
    Gionis A, Indyk P, Motwani R, et al. Similarity search in high dimensions via hashing. In VLDB. 1999; vol. 99, pp. 518–29.Google Scholar
  70. 70.
    Rosdi BA, Shing CW, Suandi SA. Finger vein recognition using local line binary pattern. Sensors. 2011;11(12):11357–71.Google Scholar
  71. 71.
    Rosten E, Drummond T. Machine learning for high-speed corner detection. In Computer Vision–ECCV 2006. Springer, 2006; pp. 430–43.Google Scholar
  72. 72.
    Chikkerur S, Cartwright AN, Govindaraju V. K-plet and coupled bfs: a graph based fingerprint representation and matching algorithm. In: Zhang D, Jain AK, editors. Advances in Biometrics, volume 3832 of Lecture Notes in Computer Science. 2005; pp. 309–15. Springer Berlin Heidelberg.Google Scholar
  73. 73.
    Cappelli R, Ferrara M, Maltoni D. Minutia cylinder-code: a new representation and matching technique for fingerprint recognition. IEEE Trans Pattern Anal Mach Intell. 2010;32(12):2128–41.Google Scholar
  74. 74.
    Cappelli R, Ferrara M, Franco A, Maltoni D. Fingerprint verification competition 2006. Biom Technol Today. 2007;15(7):7–9.Google Scholar
  75. 75.
    Cappelli R, Ferrara M, Maltoni D, Tistarelli M. Mcc: a baseline algorithm for fingerprint verification in fvc-ongoing. 11th International Conference on Control Automation Robotics Vision (ICARCV). 2010; pp. 19–23.Google Scholar
  76. 76.
    Ferrara M, Maltoni D, Cappelli R. Noninvertible minutia cylinder-code representation. IEEE Trans Inf Forensic Secur. 2012;7(6):1727–37.Google Scholar
  77. 77.
    Cappelli R, Ferrara M, Maltoni D. Fingerprint indexing based on minutia cylinder-code. IEEE Trans Pattern Anal Mach Intell. 2011;33(5):1051–7.Google Scholar
  78. 78.
    Xu H, Veldhuis RNJ, Kevenaar TAM, Akkermans TAHM, Bazen AM. Spectral minutiae: a fixed-length representation of a minutiae set. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008. CVPRW '08. June 2008; pp. 1–6.Google Scholar
  79. 79.
    Xu H, Veldhuis RNJ, Bazen AM, Kevenaar TAM, Akkermans TAHM, Gokberk B. Fingerprint verification using spectral minutiae representations. IEEE Trans Inf Forensic Secur. 2009;4(3):397–409.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Chair for Data Communications SystemsUniversity of SiegenSiegenGermany

Personalised recommendations