Robust Iris Templates for Efficient Person Identification

  • Abhishek GangwarEmail author
  • Akanksha Joshi
  • Renu Sharma
  • Zia Saquib
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 166)


Iris recognition is seen as a highly reliable biometric technology. The performance of iris recognition is severely impacted when encountering irises captured in realistic conditions. The selection of the features subset and the classification is an important issue for iris biometrics. In this paper we propose new methods for feature extraction and template creation during enrollment to improve the performance of iris recognition systems. The experiments are based on storing i) multiple templates (template group) for a user ii) Single template by taking average mean of multiple templates iii) Single template calculated from multiple templates using Direct Linear Discriminant Analysis (DLDA). We used CASIA Iris Interval database for our experiments. Experiments report significant improvement in the performance of iris recognition.


Feature Extraction Biometric Identification Wavelet Transform template creation 


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  1. 1.
    Daugman, J.G.: The importance of being random: Statistical principles of iris recognition. Pattern Recognition 36(2), 279–291 (2003)CrossRefGoogle Scholar
  2. 2.
    Daugman, J.G.: How iris recognition works. IEEE Trans. on Circuits and Systems for Video Technology 14(1), 21–30 (2004)CrossRefGoogle Scholar
  3. 3.
    Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A literature survey. ACM Computing Surveys 35(4), 399–458 (2003)CrossRefGoogle Scholar
  4. 4.
    Daugman, J.: Biometric personal identification system based on iris analysis. U.S. Patent No. 5, 291, 560 (March 1994)Google Scholar
  5. 5.
    Chuan Chen, T., Liang Chung, K.: An Efficient Randomized Algorithm for Detecting Circles. Computer Vision and Image Understanding 83, 172–191 (2001)CrossRefzbMATHGoogle Scholar
  6. 6.
    Canny, J.: A Computational Approach to Edge Detection. IEEE Transaction on Pattern Analysis and Machine Intelligence 8, 679–714 (1986)CrossRefGoogle Scholar
  7. 7.
    Yu, H., Yang, J.: A Direct LDA Algorithm for High-Dimensional Data with Application to Face Recognition Interactive System Labs. Carnegie Mellon University, PittsburghGoogle Scholar
  8. 8.
    Swets, D., Weng, J.: Using discriminant eigenfeatures for image retrieval. PAMI 18(8), 831–836 (1996)CrossRefGoogle Scholar
  9. 9.
    Chen, L., Liao, H., Ko, M., Lin, J., Yu, G.: A new lda-based face recognition system which can solve the small sample size problem. Pattern Recognition 33(10), 1713–1726 (2000)CrossRefGoogle Scholar
  10. 10.
    Wildes, R.P.: Iris recognition: an emerging biometric technology. Proceedings of the IEEE 85(9), 1348–1363 (1997)CrossRefGoogle Scholar
  11. 11.
    Frazier, M.W.: An Introduction to Wavelets through Linear algebra. Springer (1999)Google Scholar
  12. 12.
    CASIA Iris Image Database,

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  • Abhishek Gangwar
    • 1
    Email author
  • Akanksha Joshi
    • 1
  • Renu Sharma
    • 1
  • Zia Saquib
    • 1
  1. 1.Center for Development of Advanced ComputingMumbaiIndia

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