A Model Based, Anatomy Based Method for Synthesizing Iris Images

  • Jinyu Zuo
  • Natalia A. Schmid
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)

Abstract

Popularity of iris biometric grew considerably over the past 2-3 years. It resulted in development of a large number of new iris encoding and processing algorithms. Since there are no publicly available large scale and even medium size databases, neither of the algorithms has undergone extensive testing. With the lack of data, two major solutions to the problem of algorithm testing are possible: (i) physically collecting a large number of iris images or (ii) synthetically generating a large scale database of iris images. In this work, we describe a model based/anatomy based method to synthesize iris images and evaluate the performance of synthetic irises by using a traditional Gabor filter based system and by comparing local independent components extracted from synthetic iris images with those from real iris images. The issue of security and privacy is another argument in favor of generation of synthetic data.

Keywords

Independent Component Analysis Independent Component Analysis Synthetic Dataset Iris Image Minimum Euclidean Distance 
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.
    CASIA Iris Image Dataset (ver. 1.0), http://www.sinobiometrics.com/casiairis.htm
  2. 2.
    Cui, J., Wang, Y., Huang, J., Tan, T., Sun, Z.: An Iris Image Synthesis Method Based on PCA and Super-resolution. In: Proc. of the 17th Intern. Conf. on Pattern Recognition, pp. 471–474 (2004)Google Scholar
  3. 3.
    Makthal, S., Ross, A.: Synthesis of Iris Images using Markov Random Fields. In: Proc. of 13th European Signal Processing Conference (EUSIPCO), Antalya, Turkey (September 2005) (to appear)Google Scholar
  4. 4.
    Miles Research: Iris Pigmentation Research Info., http://www.milesresearch.com/iris/
  5. 5.
    Miles Research: Iris Images from Film CameraGoogle Scholar
  6. 6.
  7. 7.
    Sharan, F.: Iridology - a complete guide to diagnosing through the iris and to related forms of treatment, HarperCollins, Hammersmith, London (1992)Google Scholar
  8. 8.
    Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. John Wiley and Sons, Chichester (2001)CrossRefGoogle Scholar
  9. 9.
    Noh, S., Pae, K., Lee, C., Kim, J.: Multiresolution Independent Component Analysis for Iris Identification. In: Proc. of the Intern. Technical Conf. on Circuits Systems, Comp. and Commun., Puket, Thailand, pp. 1674–1677 (2002)Google Scholar
  10. 10.
    Bae, K., Noh, S., Kim, J.: Iris Feature Extraction Using Independent Component Analysis. In: Proc. of the 4th Intern. Conf. on Audio-and Video-Based Biometric Person Authentication, Guildford, UK, June 2003, pp. 838–844 (2003)Google Scholar
  11. 11.
    FastICA MATLAB Package. Available online at http://www.cis.hut.fi/projects/ica/fastica
  12. 12.
    Natural images. Available online at http://www.cis.hut.fi/projects/ica/imageica/
  13. 13.
    Daugman, J.: High Confidence visual Recognition of Persons by a test of Statistical Independence. IEEE Trans. on Pattern Analysis and Machine Intelligence 15(11), 1148–1161 (1993)CrossRefGoogle Scholar
  14. 14.
    Mansfield, A.J., Wayman, J.L.: Best Practices in Testing and Reporting Performance of Biometric Devices (2002). Available online at http://www.cesg.gov.uk/site/ast/biometrics/media/BestPractice.pdf
  15. 15.
    Wayman, J., Jain, A., Maltoni, D., Maio, D. (eds.): Biometric Systems: Technology, Design, and Performance Evaluation. Springer, New York (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jinyu Zuo
    • 1
  • Natalia A. Schmid
    • 1
  1. 1.Lane Department of Computer Science and Electrical EngineeringWest Virginia UniversityMorgantownUSA

Personalised recommendations