Shape Analysis of Stroma for Iris Recognition

  • S. Mahdi Hosseini
  • Babak N. Araabi
  • Hamid Soltanian-Zadeh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)

Abstract

In this paper, a new shape analysis approach for iris recognition is proposed. First, the extracted iris images from eye portrait are enhanced by image deblurring filter which computes restoration using FFT-based Tikhonov filter with the identity matrix as the regularization operator. This procedure produces a smooth image in which shape of pigmented fibro vascular tissue known as Stroma is depicted easily. Then, an adaptive filter is defined to extract these shapes. In the next step, shape analysis techniques are applied in order to extract robust features from contour of the shapes such as support functions and radius vectors. These features are invariant under iris localization and mapping. Finally, a feature strip code is defined for every iris image. Introduced algorithm is applied to UBIRIS databank. Experimental results show efficiency of the proposed method by achieving an accuracy of 95.08% on first session of UBIRIS.

Keywords

Biometric Recognition Stroma Tikhonov Filter Shape Analysis 

References

  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    Daugman, J.G.: Demodulation by complex-valued wavelets for stochastic pattern recognition. International Journal of Wavelets, Multiresolution, and Information Processing 1(1), 1–17 (2003)MATHCrossRefGoogle Scholar
  3. 3.
    Ma, L., Wang, Y., Tan, T.: Iris recognition using circular symmetric filters. In: Proceedings of the 16th International Conference on Pattern Recognition, Quebec City, Quebec, Canada, August 2002, vol. 2, pp. 414–417 (2002)Google Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    Wildes, R.P., Asmuth, J.C., Green, G.L., et al.: A machinevision system for iris recognition. Machine Vision and Applications 9(1), 1–8 (1996)CrossRefGoogle Scholar
  6. 6.
    Tisse, C., Martin, L., Torres, L., Robert, M.: Person identification technique using human iris recognition. In: VI 2002. Proceedings of the 15th International Conference on Vision Interface, Calgary, Canada, May 2002, pp. 294–299 (2002)Google Scholar
  7. 7.
    Lim, S., Lee, K., Byeon, O., Kim, T.: Efficient iris recognition through improvement of feature vector and classifier. ETRI Journal 23(2), 61–70 (2001)Google Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    Bae, K., Noh, S.-I., Kim, J.: Iris feature extraction using independent component analysis. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 838–844. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  10. 10.
    Gu, H.-Y., Zhuang, Y.-T., Pan, Y.-H.: An iris recognition method based on multi-orientation features and nonsymmetrical SVM. Journal of Zhejiang University: Science 6A(5), 428–432 (2005)CrossRefGoogle Scholar
  11. 11.
    Imesch, P.D., Wallow, I.H.L., Albert, D.M.: The color of the human eye: A review of morphologic correlates and of some conditions that affect iridial pigmentation. Survey of Ophthal. 41, 117–123 (1997)CrossRefGoogle Scholar
  12. 12.
    Proença, H., Alexandre, L.A.: UBIRIS: a noisy iris image database. In: Roli, F., Vitulano, S. (eds.) ICIAP 2005. LNCS, vol. 3617, pp. 3–540. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  13. 13.
    Kindratenko, V.: Development and Application of Image Analysis Techniques for identification and Classification of Microscopic Particles. Ph.D. Thesis, University of Antwerp, Belgium (1997), http://www.ncsa.uiuc.edu/~kindr/phd/index.pdf
  14. 14.
    Stoyan, D., Stoyan, H.: Fractals, Random Shapes and Point Fields (Methods of Geometrical Statistics). John Wiley & Sons, Chichester (1995)Google Scholar
  15. 15.
    Hosseini, S.M., Araabi, B.N., Poursaberi, A.: A Super Fast and Accurate method for Iris Segmentation Based on Effect of Retina Color on Pupil. In: 2nd IAPR/IEEE International Conference on Biometrics, Seoul, Korea, August 27-29, 2007 (submitted)Google Scholar
  16. 16.
    Proença, H., Alexandre, L.A.: Iris segmentation methodology for non-cooperative Recognition. IEE Proc.-Vis. Image Signal Process 153(2) (April 2006)Google Scholar
  17. 17.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice Hall Inc, Englewood Cliffs (2002)Google Scholar
  18. 18.
    Hansen, P.C., Nagy, J.G., O’Leary, D.P.: Deblurring Images - Matrices, Spectra and Filtering, SIAM, Philadelphia (2006)Google Scholar
  19. 19.
    Institute of Automation, Chinese Academy of Sciences, CASIA Iris Image Database, CASIA-IrisV1 and CASIA-IrisV3: http://www.cbsr.ia.ac.cn/IrisDatabase.htm

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • S. Mahdi Hosseini
    • 1
  • Babak N. Araabi
    • 1
  • Hamid Soltanian-Zadeh
    • 1
    • 2
  1. 1.Control and Intelligent Processing Center of Excellence, School of ECE, Univesity of, Tehran, P.O. Box 14395-515, TehranIran
  2. 2.Image Analysis Lab., Radiology Dept., Henry Ford Health System, Detroit, MI 48202USA

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