Skip to main content

Iris Spoofing: Reverse Engineering the Daugman Feature Encoding Scheme

  • Chapter
  • First Online:
Handbook of Iris Recognition

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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 2006

    Google Scholar 

  2. Makthal, S., Ross, A.: Synthesis of Iris images using Markov Random fields. In: Proceedings of 13th European Signal Processing Conference, Antalya, Turkey, September 2005

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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 1-59593-201-1

    Chapter  Google Scholar 

  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. 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. 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. 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, 2003

    Google Scholar 

  11. Daugman, J.: How iris recognition works. Proc. Int. Conf. Image Process. 1, I-33–I-36 (2002)

    Google Scholar 

  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. http://iris.nist.gov/ice/

  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)

    Article  Google Scholar 

  15. Bowyer, K., Hollingsworth, K., Flynn, P.: Image understanding for iris biometrics: a survey. Comput. Vis. Image Underst. 110, 281–307 (2008)

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Marios Savvides .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag London

About this chapter

Cite this chapter

Venugopalan, S., Savvides, M. (2013). Iris Spoofing: Reverse Engineering the Daugman Feature Encoding Scheme. In: Burge, M., Bowyer, K. (eds) Handbook of Iris Recognition. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-4402-1_18

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-4402-1_18

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-4401-4

  • Online ISBN: 978-1-4471-4402-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics