Iris Matching by Local Extremum Points of Multiscale Taylor Expansion

  • Algirdas Bastys
  • Justas Kranauskas
  • Rokas Masiulis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)


Random distribution of features in iris image texture allows to perform iris-based personal authentication with high confidence. We propose to use the most significant local extremum points of the first two Taylor expansion coefficients as descriptors of the iris texture. A measure of similarity that is robust to moderate inaccuracies in iris segmentation is presented for the proposed features. We provide experimental results of verification quality for four commonly used iris data-sets. Strong and weak aspects of the proposed approach are also discussed.


Local Extremum Iris Image Equal Error Rate Iris Recognition Local Extremum Point 
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.


  1. 1.
    Daugman, J., Dowing, C.: Epigenetic randomness, complexity, and singularity of human iris patterns. In: Proceedings of the Royal Society, B, 268, Biological Sciences, pp. 1737–1740 (2001)Google Scholar
  2. 2.
    Daugman, J.: Statistical richness of visual phase information: update on recognizing persons by iris patterns. Int. J. Comput. Vis. 45(1), 25–38 (2001)Google Scholar
  3. 3.
    Daugman, J.: High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(11), 1148–1161 (1993)Google Scholar
  4. 4.
    Daugman, J.: Results from 200 billion iris cross-comparisons. Technical Report UCAM-CL-TR-635 ISSN 1476-2986 (2005),
  5. 5.
    Aoptix: Breakthrough in iris recognition (2007),
  6. 6.
    Bastys, A., Kranauskas, J., Masiulis, R.: Iris recognition by local extremum points of multiscale Taylor expansion (2008),
  7. 7.
    Chinese Academy of Sciences - Institute of Automation Iris Database 1.0 (2003),
  8. 8.
    Chinese Academy of Sciences - Institute of Automation Iris Database 3.0 (2005),
  9. 9.
    National Institute of Science and Technology (NIST): Iris Challenge Evaluation (2005),
  10. 10.
    Metz, C.E.: Basic principles of ROC analysis. Semin. Nucl. Med. 8, 283–298 (1978)Google Scholar
  11. 11.
    Monro, D.M., Rakshit, S., Zhang, D.: DCT-Based Iris Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(4), 586–595 (2007)Google Scholar
  12. 12.
    Miyazawa, K., Ito, K., Aoki, T., Kobayashi, K., Nakajima, H.: A Phase-Based Iris Recognition Algorithm. In: Zhang, D., Jain, A.K. (eds.) ICB 2006. LNCS, vol. 3832, pp. 356–365. Springer, Heidelberg (2005)Google Scholar
  13. 13.
    Bae, K., Noh, S., Kim, J.: Iris feature extraction using independent component analysis. In: Proc. 4th Int. Conf. Audio- and Video-Based Biometric Person Authentication, pp. 838–844 (2003)Google Scholar
  14. 14.
    Ma, L.: Person identification based on iris recognition, Ph.D dissertation, Inst. Automation, Chinese Academy of Sciences, Beijing, China (2003)Google Scholar
  15. 15.
    Ma, L., Tan, T., Wang, Y., Zhang, D.: Efficient Iris Recognition by Characterizing Key Local Variations. IEEE Transactions on Image Processing 13(6) (2004)Google Scholar
  16. 16.
    Phillips, P.J., Bowyer, K.W., Flynn, P.J.: Comments on the CASIA version 1.0 Iris Data Set. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(10), 1869–1870 (2007)Google Scholar
  17. 17.
    Daugman, J.: New Methods in Iris Recognition. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics 37(5), 1167–1175 (2007)Google Scholar
  18. 18.
    Daugman, J.: Flat ROC Curves, Steep Predictive Quality Metrics: Response to NISTIR-7440 and FRVT/ICE2006 Reports (2007),

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Algirdas Bastys
    • 1
  • Justas Kranauskas
    • 1
  • Rokas Masiulis
    • 1
  1. 1.Department of Computer Science II, Faculty of Mathematics and InformaticsVilnius UniversityLithuania

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