Skip to main content
Springer Nature Link
Log in

A Novel and Efficient Method to Extract Features and Vector Creation in Iris Recognition System

  • Conference paper
KI 2009: Advances in Artificial Intelligence (KI 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5803))

Included in the following conference series:

  • 1690 Accesses

Abstract

The selection of the optimal feature subset and the classification has become an important issue in the field of iris recognition. In this paper we propose several methods for iris feature subset selection and vector creation. In this paper we propose a new feature extraction method for iris recognition based on contourlet transform. Contourlet transform captures the intrinsic geometrical structures of iris image. For reducing the feature vector dimensions we use the method for extract only significant bit and information from normalized iris images. In this method we ignore fragile bits. At last, the feature vector is created by two methods: Co-occurrence matrix properties and contourlet coefficients. For analyzing the desired performance of our proposed method, we use the CASIA dataset, which is comprised of 108 classes with 7 images in each class and each class represented a person. Experimental results show that the proposed increase the classification accuracy and also the iris feature vector length is much smaller versus the other methods.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Wildes, R.P.: Iris recognition: an emerging biometric technology. Proceedings of the IEEE 85(9), 1348–1363 (1997)

    Article  Google Scholar 

  2. Jain, A., Bolle, R., Pankanti, S.: Biometrics: Personal Identification in a Networked Society. Kluwer Academic Publishers, Norwell (1999)

    Book  Google Scholar 

  3. Daugman, J.: Biometric personal identification system based on iris analysis, US patent no. 5291560 (1994)

    Google Scholar 

  4. Mansfield, T., Kelly, G., Chandler, D., Kane, J.: Biometric product testing. Final Report, National Physical Laboratory, Middlesex, U.K (2001)

    Google Scholar 

  5. Daugman, J.G.: 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)

    Article  Google Scholar 

  6. Daugman, J.: Demodulation by complex-valued wavelets for stochastic pattern recognition. International Journal of Wavelets, Multiresolution and Information Processing 1(1), 1–17 (2003)

    Article  MATH  Google Scholar 

  7. CASIA, Chinese Academy of Sciences – Institute of Automation. Database of 756 Grayscale Eye Images. Versions 1.0 (2003), http://www.sinobiometrics.com

  8. He, X., Shi, P.: An efficient iris segmentation method for recognition. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds.) ICAPR 2005. LNCS, vol. 3687, pp. 120–126. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  9. Bae, K., Noh, S., Kim, J.: Iris feature extraction using independent component analysis. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 1059–1060. Springer, Heidelberg (2003)

    Google Scholar 

  10. Boles, W.W., Boashash, B.: A human identification technique using images of the iris and wavelet transform. IEEE Transactions on Signal Processing 46(4), 1185–1188 (1998)

    Article  Google Scholar 

  11. Chong, S.C., Teoh, A.B.J., Ngo, D.C.L.: Iris authentication using privatized advanced correlation filter. In: Zhang, D., Jain, A.K. (eds.) ICB 2005. LNCS, vol. 3832, pp. 382–388. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  12. Daugman, J.: Statistical richness of visual phase information: update on recognizing persons by iris patterns. International Journal of Computer Vision 45(1), 25–38 (2001)

    Article  MATH  Google Scholar 

  13. He, X., Shi, P.: An efficient iris segmentation method for recognition. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds.) ICAPR 2005. LNCS, vol. 3687, pp. 120–126. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  14. Jeong, D.S., Park, H.-A., Park, K.R., Kim, J.: Iris recognition in mobile phone based on adaptive Gabor filter. In: Zhang, D., Jain, A.K. (eds.) ICB 2005. LNCS, vol. 3832, pp. 457–463. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  15. Vijaya Kumar, B.V.K., Xie, C., Thornton, J.: Iris verification using correlation filters. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 697–705. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  16. Lee, E.C., Park, K.R., Kim, J.: Fake iris detection by using purkinje image. In: Zhang, D., Jain, A.K. (eds.) ICB 2005. LNCS, vol. 3832, pp. 397–403. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  17. Lim, S., Lee, K., Byeon, O., Kim, T.: Efficient iris recognition through improvement of feature vector and classifier. Electronics and Telecommunications Research Institute Journal 23(2), 61–70 (2001)

    Google Scholar 

  18. Liu, X., Bowyer, K.W., Flynn, P.J.: Experiments with an improved iris segmentation algorithm. In: Proceedings of the 4th IEEE Workshop on Automatic Identification Advanced Technologies (AUTO ID 2005), Buffalo, NY, USA, October 2005, pp. 118–123 (2005)

    Google Scholar 

  19. Liu, X., Bowyer, K.W., Flynn, P.J.: Experimental evaluation of iris recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), San Diego, Calif, USA, June 2005, vol. 3, pp. 158–165 (2005)

    Google Scholar 

  20. Ma, L., Tan, T., Wang, Y., Zhang, D.: Personal identification based on iris texture analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(12), 1519–1533 (2003)

    Article  Google Scholar 

  21. Daugman, J.: How Iris Recognition works. IEEE Transactions on Circuits and systems for video Technology 14(1), 21–30 (2004)

    Article  Google Scholar 

  22. Ali, J.M.H., Hussanien, A.E.: An Iris Recognition System to Enhance E-security Environment Based on Wavelet Theory. AMO - Advanced Modeling and Optimization 5(2) (2003)

    Google Scholar 

  23. Do, M.N., Vetterli, M.: The contourlet transform: An Efficient directional multiresolution image representation. IEEE Transactions on Image Processing 14(12), 2091–2106

    Google Scholar 

  24. Burt, P.J., Adelson, E.H.: The Laplacian pyramid as a compact image code. IEEE Trans. Commun. 31(4), 532–540 (1983); Bamberger, R.H., Smith, M.J.T.: A filter bank for the directional decomposition of images: Theory and design. IEEE Trans. Signal Proc. 40(4), 882–893 (1992)

    Article  Google Scholar 

  25. Hollingsworth, K.P., Bowyer, K.W.: The Best Bits in an Iris Code. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) (April 2008)

    Google Scholar 

  26. Haralick, R.M., Shanmugam, K., Din, L.: Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics 3(6), 610–621 (1973)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Islamic Azad University Qazvin Branch, Iran

    Amir Azizi

  2. Ferdowsi University of Mashhad, Iran

    Hamid Reza Pourreza

Authors
  1. Amir Azizi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hamid Reza Pourreza
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. GET Lab, University of Paderborn, Pohlweg 47-49, 33098, Paderborn, Germany

    Bärbel Mertsching , Marcus Hund  & Zaheer Aziz ,  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Azizi, A., Pourreza, H.R. (2009). A Novel and Efficient Method to Extract Features and Vector Creation in Iris Recognition System. In: Mertsching, B., Hund, M., Aziz, Z. (eds) KI 2009: Advances in Artificial Intelligence. KI 2009. Lecture Notes in Computer Science(), vol 5803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04617-9_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-04617-9_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04616-2

  • Online ISBN: 978-3-642-04617-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics