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Iris Anti-spoofing

  • Zhenan SunEmail author
  • Tieniu Tan
Chapter
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

Iris images contain rich texture information for reliable personal identification. However, forged iris patterns may be used to spoof iris recognition systems. This paper proposes an iris anti-spoofing approach based on the texture discrimination between genuine and fake iris images. Four texture analysis methods include gray level co-occurrence matrix, statistical distribution of iris texture primitives, local binary patterns (LBP) and weighted-LBP are used for iris liveness detection. And a fake iris image database is constructed for performance evaluation of iris liveness detection methods. Fake iris images are captured from artificial eyeballs, textured contact lens and iris patterns printed on a paper, or synthesised from textured contact lens patterns. Experimental results demonstrate the effectiveness of the proposed texture analysis methods for iris liveness detection. And the learned statistical texture features based on weighted-LBP can achieve 99accuracy in classification of genuine and fake iris images.

Keywords

Local Binary Pattern Iris Image Sift Descriptor Iris Recognition Local Binary Pattern Feature 
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.
    Ruiz-Albacete V, Tome-Gonzalez P, Alonso-Fernandez F, Galbally J, Fierrez J, Ortega-Garcia J (2008) Direct attacks using fake images in iris verification. In: Schouten B, Juul N, Drygajlo A, Tistarelli M (eds) Biometrics and identity management, vol 5372., Lecture Notes in Computer ScienceSpringer, Berlin Heidelberg, pp 181–190CrossRefGoogle Scholar
  2. 2.
    Schneier B (1999) Inside risks: the uses and abuses of biometrics. Communications of the ACM 42(8):136CrossRefGoogle Scholar
  3. 3.
    Schuckers SAC (2002) Spoofing and anti-spoofing measures. Inf Secur Tech Rep 7(4):56–62CrossRefGoogle Scholar
  4. 4.
    Thalheim L, Krissler J, Ziegler P (2002) Body check: biometrics defeated’c’t magazine. June 3, 2002Google Scholar
  5. 5.
    Matsumoto T (2004) Artificial irises: importance of vulnerability analysis. In: Proceedings of Asian biometrics workshop (AWB), vol. 45Google Scholar
  6. 6.
    Szwoch M, Pieniazek P (2012) Computer Vision and Graphics. Eye blink based detection of liveness in biometric authentication systems using conditional random fields. Springer, Berlin, pp 669–676Google Scholar
  7. 7.
    Adler F (1965) Physiology of the eye: Clinical application the cv mosby companyGoogle Scholar
  8. 8.
    Davision H (1962) The eye. Academic, LondonGoogle Scholar
  9. 9.
    Huang X, Ti C, Hou Qz, Tokuta A, Yang R (2013) An experimental study of pupil constriction for liveness detection. In: IEEE Workshop on Applications of Computer Vision (WACV), pp 252–258Google Scholar
  10. 10.
    Puhan N, Sudha N, Suhas Hegde A (2011) A new iris liveness detection method against contact lens spoofing. In: IEEE International Symposium on Consumer Electronics (ISCE), pp 71–74Google Scholar
  11. 11.
    Daugman J (2003) Demodulation by complex-valued wavelets for stochastic pattern recognition. Int J Wavelets Multiresolut Inf Proc 1(01):1–17CrossRefzbMATHGoogle Scholar
  12. 12.
    Lee E, Park K, Kim J (2005) Fake iris detection by using purkinje image. In: Zhang D, Jain A (eds) Advances in biometrics, vol 3832., Lecture Notes in Computer Science.Springer, Berlin Heidelberg, pp 397–403CrossRefGoogle Scholar
  13. 13.
    Galbally J, Ortiz-Lopez J, Fierrez J, Ortega-Garcia J (2012) Iris liveness detection based on quality related features. In: IAPR International conference on biometrics (ICB). IEEE pp 271–276Google Scholar
  14. 14.
    He X, An S, Shi P (2007) Statistical texture analysis-based approach for fake iris detection using support vector machines. In: Lee SW, Li S (eds) Advances in biometrics, vol 4642., Lecture Notes in Computer Science.Springer, Berlin, pp 540–546CrossRefGoogle Scholar
  15. 15.
    Wei Z, Qiu X, Sun Z, Tan T (2008) Counterfeit iris detection based on texture analysis. In: International conference on pattern recognition (ICPR), pp 1–4Google Scholar
  16. 16.
    He Z, Sun Z, Tan T, Wei Z (2009) Efficient iris spoof detection via boosted local binary patterns. In: Tistarelli M, Nixon M (eds) Advances in biometrics, vol 5558., Lecture Notes in Computer Science.Springer, Berlin, pp 1080–1090CrossRefGoogle Scholar
  17. 17.
    Zhang H, Sun Z, Tan T (2010) Contact lens detection based on weighted LBP. In: International conference on pattern recognition (ICPR), pp 4279–4282Google Scholar
  18. 18.
    Doyle JS, Flynn PJ, Bowyer KW (2013) Automated classification of contact lens type in iris images. In: IAPR International conference on biometricsGoogle Scholar
  19. 19.
    Doyle JS, Bowyer KW, Flynn PJ (2013) Variation in accuracy of textured contact lens detection based on sensor and lens pattern. In: IEEE International conference on biometrics: theory applications and systems (BTAS), pp 1–6Google Scholar
  20. 20.
    Lee SJ, Park KR, Kim J (2006) Robust fake iris detection based on variation of the reflectance ratio between the iris and the sclera. In: Biometrics symposium: special session on research at the biometric consortium conference 2006:1–6CrossRefzbMATHGoogle Scholar
  21. 21.
    Lee SJ, Park KR, Lee YJ, Bae K (2007) Multi-feature based fake iris detection method. Opt Eng 46(12):127–204Google Scholar
  22. 22.
    Chen R, Lin X, Ding T (2012) Liveness detection for iris recognition using multispectral images. Patt Recogn Lett 33(12):1513–1519CrossRefGoogle Scholar
  23. 23.
    Connell J, Ratha N, Gentile J, Bolle R (2013) Fake iris detection using structured light. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 8692–8696Google Scholar
  24. 24.
    Haralick R, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Sys Man Cybern SMC-3(6) 610–621Google Scholar
  25. 25.
    Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Patt Anal Mach Intell 24(7):971–987CrossRefGoogle Scholar
  26. 26.
    He Z, Sun Z, Tan T, Qiu X, Zhong C, Dong W (2008) Boosting ordinal features for accurate and fast iris recognition. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 1–8Google Scholar
  27. 27.
    Schapire R, Singer Y (1999) Improved boosting algorithms using confidence-rated predictions. Mach Learn 37(3):297–336CrossRefzbMATHGoogle Scholar
  28. 28.
    Lowe D (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60(2):91–110CrossRefGoogle Scholar
  29. 29.
    Dobes M, Machala L, Upol iris database. http://www.inf.upol.cz/iris/
  30. 30.
    Wei Z, Tan T, Sun Z (2008) Synthesis of large realistic iris databases using patch-based sampling. In: International conference on pattern recognition (ICPR), pp 1–4Google Scholar

Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern RecognitionInstitute of Automation, Chinese Academy of SciencesBeijingP.R. China

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