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Iris Spoofing: Reverse Engineering the Daugman Feature Encoding Scheme

  • Shreyas Venugopalan
  • Marios Savvides
Chapter
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

Biometric systems based on iridal patterns have shown very high accuracies in verifying an individual’s identity due to the uniqueness of the iris pattern across individuals. For identity verification purposes, only the iris bit code template of an individual need be stored. In this chapter, we explore methods to generate synthetic iris textures corresponding to a given person for the purpose of bypassing an iris-based security system using these iris templates. We present analysis to prove that when this “spoof” texture is presented to an iris recognition system; it will elicit a similar response from the system as that due to the genuine iris texture to which the spoof corresponds. We embed this spoof texture within the iris of an imposter to achieve this end. Systems using filter-based feature extraction systems – such as Daugman style systems – may be bypassed using this technique. We assume knowledge of solely the feature extraction mechanism of the iris matching scheme and, as mentioned, the iris bit code template of the person whose iris is to be spoofed. We present a complete investigation into how one can get by an iris recognition system using this approach, by generating various “natural”-looking irises and hope to use this knowledge to incorporate several countermeasures into the feature extraction scheme of an iris recognition module.

Keywords

Receiver Operating Characteristic Curve Iris Image Iris Recognition Gabor Function Background Texture 
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.

Notes

Acknowledgment

This research was supported by CyLab at Carnegie Mellon under grants DAAD19-02-1-0389 and W911NF-09-1-0273 from the Army Research Office.

References

  1. 1.
    Shah, S., Ross, A.: Generating synthetic irises by feature agglomeration. In: Proceedings of International Conference on Image Processing, pp. 317–320. Atlanta, GA, October 2006Google Scholar
  2. 2.
    Makthal, S., Ross, A.: Synthesis of Iris images using Markov Random fields. In: Proceedings of 13th European Signal Processing Conference, Antalya, Turkey, September 2005Google Scholar
  3. 3.
    Zuo, J., Schmid, N., Chen, X.: On generation and analysis of synthetic iris images. IEEE Trans. Inform. Forensics Secur. 2(1), 77–90 (2007)CrossRefGoogle Scholar
  4. 4.
    Lefohn, A., Caruso, R., Reinhard, E., Budge, B.: An ocularist’s approach to human iris synthesis. IEEE Comput. Graph. Appl. 23(6), 70–75 (2003)CrossRefGoogle Scholar
  5. 5.
    Cui, J., Wang, Y., Huang, J., Tan, T., Sun, Z.: An Iris image synthesis method based on PCA and super-resolution. In: Proceedings of 17th International Conference on Pattern Recognition, vol. 4, pp. 471–474. Cambridge, UK (2004)Google Scholar
  6. 6.
    Wecker, L., Samavati, F., Gavrilova, M.: Iris synthesis: a reverse subdivision application. In: Proceedings of the 3rd International Conference on Computer Graphics and Interactive Techniques in Australasia and South East Asia, pp. 121–125. (2005). ISBN 1-59593-201-1CrossRefGoogle Scholar
  7. 7.
    Zuo, J., Schmid, N., Chen, X.: On performance comparison of real and synthetic iris images. IEEE Int. Conf. Image Process. 1, 305–308 (2006)Google Scholar
  8. 8.
    Adler, A.: Sample images can be independently restored from face recognition templates. IEEE Can. Conf. Elect. Comput. Eng. 2, 1163–1166 (2003)Google Scholar
  9. 9.
    Adler, A.: Images can be regenerated from quantized biometric match score data. IEEE Can. Conf. Elect. Comput. Eng. 1, 469–472 (2004)Google Scholar
  10. 10.
    Masek, L., Kovesi, P.: MATLAB source code for a biometric identification system based on Iris patterns. The School of Computer Science and Software Engineering, The University of Western Australia, 2003Google Scholar
  11. 11.
    Daugman, J.: How iris recognition works. Proc. Int. Conf. Image Process. 1, I-33–I-36 (2002)Google Scholar
  12. 12.
    Daugman, J.: Probing the uniqueness and randomness of IrisCodes: results from 200 billion iris pair comparisons. Proc. IEEE 94(11), 1927–1935 (2006)Google Scholar
  13. 13.
  14. 14.
    Venugopalan, S., Savvides, M.: How to generate spoofed irises from an iris code template. IEEE Trans. Inform. Forensics Secur. 6(2), 385–395 (2011)CrossRefGoogle Scholar
  15. 15.
    Bowyer, K., Hollingsworth, K., Flynn, P.: Image understanding for iris biometrics: a survey. Comput. Vis. Image Underst. 110, 281–307 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.Cylab Biometrics Center, Department of Electrical and Computer EngineeringCarnegie Mellon UniversityPittsburghUSA

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