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Fake Iris Detection Based on Multiple Wavelet Filters and Hierarchical SVM

  • Kang Ryoung Park
  • Min Cheol Whang
  • Joa Sang Lim
  • Yongjoo Cho
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4296)

Abstract

With the increasing needs for higher security level, biometric systems have been widely used for many applications. Among biometrics, iris recognition system has been in the limelight for high security applications. Until now, most researches have been focused on iris identification algorithm and iris camera system. However, after the recent report of attacking iris recognition system by fake iris such as printed, photography and contact lens iris has been disclosed, the importance of fake iris detection is much increased.

So, we propose the new method of detecting fake iris. This research has following three advances compared to previous works. First, to detect fake iris, we check both the size change of pupil and the change of iris features in local iris area (near pupil boundary) by visible light. Second, to detect the change of local iris features, we used multiple wavelet filters having Gabor and Daubechies bases. Third, to enhance the detecting accuracy of fake iris, we used a hierarchical SVM (Support Vector Machine) based on extracted wavelet features.

Keywords

Iris Recognition Fake Iris Detection multiple wavelet filters hierarchical SVM 

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References

  1. 1.
    Daugman, J.G.: High Confidence Visual Recognition of Personals by a Test of Statistical Independence. IEEE Trans. Pattern Anal. Machine Intell. 15(11), 1148–1160 (1993)CrossRefGoogle Scholar
  2. 2.
    http://www.iris-recognition.org (accessed, 24.08.2006)
  3. 3.
    Jack, K.: Video Demystified. Harris (1996)Google Scholar
  4. 4.
    Jain, A.K.: Biometrics: Personal Identification in Networked Society. kluwer academic publishers, Dordrecht (1998)Google Scholar
  5. 5.
    Smart Cards and Biometrics in Privacy-Sensitve Secure Personal Identification Systems. A Smart Card Alliance White Paper (May 2002)Google Scholar
  6. 6.
    Jain, R.: Machine Vision. McGraw-Hill International Edition (1995)Google Scholar
  7. 7.
    Mansfield, T., et al.: "Biometric Product Testing Final Report," Draft 0.6, National Physical Laboratory (March 2001)Google Scholar
  8. 8.
    Chapra, S.C., Canale, R.P.: Numerical Methods for Engineers. McGraw-Hill International Editions (1989)Google Scholar
  9. 9.
    Gonzalez, R.C., et al.: Digital Image Processing. Addison-Wesley, Reading (1992)Google Scholar
  10. 10.
    Ioammou, D., Huda, W., Laine, A.F.: Circle Recognition through a 2D Hough transform and Radius Histogramming. Image and Vision Computing 17, 15–26 (1999)CrossRefGoogle Scholar
  11. 11.
    Park, K.R.: Facial and Eye Gaze Detection. In: Bülthoff, H.H., Lee, S.-W., Poggio, T.A., Wallraven, C. (eds.) BMCV 2002. LNCS, vol. 2525, pp. 368–376. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    Park, K.R., Kim, J.: A Real-time Focusing Algorithm for Iris Recognition Camera. IEEE Transactions on System, Man and Cybernatics, Part C 35(3), 441–444 (2005)CrossRefGoogle Scholar
  13. 13.
    Park, H.-A., Park, K.R.: Iris Recognition Based on Score Level Fusion by Using SVM. Pattern Recognition Letters (submitted)Google Scholar
  14. 14.
    Chapra, S.C., et al.: Numerical Methods for Engineers. McGraw-Hill, New York (1989)Google Scholar
  15. 15.
  16. 16.
    Daugman, J.: Demodulation by complex-valued wavelets for stochastic pattern recognition. International Journal of Wavelets, Multi-resolution and Information Processing 1(1), 1–17 (2003)zbMATHCrossRefGoogle Scholar
  17. 17.
    Vogel, et al.: Optical Properties of Human Sclera and Their Consequences for Transscleral Laser Applications. Lasers in Surgery and Medicine 11(4), 331–340 (1991)CrossRefGoogle Scholar
  18. 18.
    Deng, J., et al.: Region-based Template Deformation and Masking for Eye Feature Extraction and Description. Pattern Recognition 30(3), 403–419 (1997)CrossRefGoogle Scholar
  19. 19.
    Kee, G., Byun, Y., Lee, K., Lee, Y.: Improved Technique for an Iris Recognition System with High Performance. In: AI 2001: Advances in Artificial Intelligence, pp. 177–188 (2001)Google Scholar
  20. 20.
    Mallet, S.G.: A Theory for Multi-resolution Signal Decomposition: The Wavelet Representation. IEEE Trans. on Pattern Analysis and Machine Intelligence 11(4), 674–693 (1989)CrossRefGoogle Scholar
  21. 21.
    Learned, R.E., Karl, W.C., Willsky, A.S.: Wavelet Packet based on Transient Signal Classification. In: Proc. of IEEE Conference on Time Scale and Time Frequency Analysis, pp. 109–112 (1992)Google Scholar
  22. 22.
    Jang, J., Park, K.R., Son, J., Lee, Y.: Multi-unit Iris Recognition by Image Checking Algorithm. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 450–457. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  23. 23.
    Vapnik: The Nature of Statistical Learning Theory. Springer, New York (1995)zbMATHGoogle Scholar
  24. 24.
    Vapnik: Statistical Learning Theory. Wiley-Interscience publication, Chichester (1998)zbMATHGoogle Scholar
  25. 25.
    Saunders, Support Vector Machine User Manual, RHUL, Technical Report (1998)Google Scholar
  26. 26.
    Daugman, J.: Biometric Decision Landscape, Technical Report No. TR482, University of Cambridge Computer Laboratory (2000)Google Scholar
  27. 27.
    Matsushita, Iris Image Capturing Device and Iris Image Authentication Device, Japanese Patent (Issued Number: 2002-247529)Google Scholar
  28. 28.
    Lee, E.C., Park, K.R., Kim, J.: Fake Iris Detection By Using the Purkinje Image. In: Zhang, D., Jain, A.K. (eds.) ICB 2005. LNCS, vol. 3832, pp. 397–403. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  29. 29.
    Park, K.R.: Robust Fake Iris Detection. In: Perales, F.J., Fisher, R.B. (eds.) AMDO 2006. LNCS, vol. 4069, pp. 10–18. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  30. 30.
    Park, K.R.: New Automated Iris Image Acquisition Method. Applied Optics 44(5), 713–734 (2005)CrossRefGoogle Scholar
  31. 31.
    Jang, Y.K., et al.: Robust Eyelid Detection for Iris Recognition. Journal of Institute of Electronics Engineers of Korea (submitted)Google Scholar
  32. 32.
    Kang, B.J., Park, K.R.: A Study on Fast Iris Restoration Based on Focus Checking. In: Perales, F.J., Fisher, R.B. (eds.) AMDO 2006. LNCS, vol. 4069, pp. 19–28. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  33. 33.
    http://www.polhemus.com (accessed, 24.08.2006)

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kang Ryoung Park
    • 1
    • 2
  • Min Cheol Whang
    • 1
  • Joa Sang Lim
    • 1
  • Yongjoo Cho
    • 1
  1. 1.Division of Media TechnologySangmyung UniversitySeoulKorea
  2. 2.Biometrics Engineering Research CenterSeoulKorea

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