Fusion of Dual-Tree Complex Wavelets and Local Binary Patterns for Iris Recognition

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 248)

Abstract

Iris, the most exclusive biometric trait, is a significant begetter of research since late 1980s. In this paper, we propose new feature fusion methodology based on Canonical Correlation Analysis to combine DTCW and LBP. Complex Wavelet Transform is used as an abstract level texture descriptor that gives a global scale invariant representation, while Local Binary Pattern (LBP) lay emphasis on local structures of the iris. In the proposed framework, CCA maximizes the information from the above two feature vectors which yield a more robust and compact representation for iris recognition. Experimental results demonstrate that fusion of Wavelet and LBP features using CCA attains 98.2% recognition accuracy and an EER of 1.8% on publicly available CASIA IrisV3-LAMP dataset [19].

Keywords

Biometrics Iris Recognition Dual Tree Complex Wavelet Transform Local Binary Pattern Canonical Correlation Analysis Cosine similarity measure 

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References

  1. 1.
    Global biometrics market revenue to reach $20 billion by 2018, http://www.biometricupdate.com
  2. 2.
    Daugman, J.: How Iris Recognition Works. IEEE Trans. on Circuits & Systems for Video Technology 14(1) (January 2004)Google Scholar
  3. 3.
  4. 4.
    Kim, J., Cho, S., Choi, J., Marks II, R.: Iris recognition using wavelet features. Journal of VLSI Signal Processing Systems 38(2) (2004)Google Scholar
  5. 5.
    Miyazawa, K., Ito, K., Aoki, T., Kobayashi, K.: An efficient iris recognition algorithm using phase-based image matching. In: Proc. of IEEE Int. Conf. on Image Processing, vol. 2 (2005)Google Scholar
  6. 6.
    Boles, W., Boashash, B.: A human identification technique using images of the iris and wavelet transform. IEEE Transactions on Signal Processing 46(4) (1998)Google Scholar
  7. 7.
    Sun, Z., Wang, Y., Tan, T., Cui, J.: Cascading statistical and structural classifiers for iris recognition. In: International Conference on Image Processing (2004)Google Scholar
  8. 8.
    Sun, Z., Wang, Y., Tan, T., Cui, J.: Improving iris recognition accuracy via cascaded classifiers. IEEE Trans. Syst. Man Cyber. 35(3), 435–441 (2005)CrossRefGoogle Scholar
  9. 9.
    Sun, Z., Tan, T., Qiu, X.: Graph matching iris image blocks with local binary pattern. In: Zhang, D., Jain, A.K. (eds.) ICB 2005. LNCS, vol. 3832, pp. 366–372. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Zhang, P.-F., Li, D.-S., Wang, Q.: A novel iris recognition method based on feature fusion. In: International Conference on Machine Learning and Cybernetics, pp. 3661–3665 (2004)Google Scholar
  11. 11.
    Vatsa, M., Singh, R., Noore, A.: Reducing the false rejection rate of iris recognition using textural and topological features. Int. Journal of Signal Processing 2(2), 66–72 (2005)Google Scholar
  12. 12.
    Park, C.-H., Lee, J.-J.: Extracting and combining multimodal directional iris features. In: Zhang, D., Jain, A.K. (eds.) ICB 2005. LNCS, vol. 3832, pp. 389–396. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  13. 13.
    Mehrotra, H., Majhi, B., Gupta, P.: Annular iris recognition using SURF. In: Chaudhury, S., Mitra, S., Murthy, C.A., Sastry, P.S., Pal, S.K. (eds.) PReMI 2009. LNCS, vol. 5909, pp. 464–469. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
  15. 15.
    Selesnick, I.W.: Hilbert Transform Pairs of Wavelet Bases. IEEE Sig. Proc. Letters (2001)Google Scholar
  16. 16.
  17. 17.
    Ojala, T., Pietikinen, M., Menp, T.: Multiresolution gray-scale and rotation invariant texture classification with Local Binary Patterns. IEEE Trans. on PAMI 24(7), 971–987 (2002)CrossRefGoogle Scholar
  18. 18.
    Selesnick, I.W., Baraniuk, R.G., Kingsbury, N.C.: The dual-tree complex wavelet transform. IEEE Signal Processing Magazine 2(2), 123–151 (2005)CrossRefGoogle Scholar
  19. 19.
  20. 20.
    Sun, Q.-S., Liu, Z.-D., Heng, P.-A., Xia, D.-S.: A theorem on the generalized canonical projective vectors. Pattern Recognition 38, 449–452 (2005)CrossRefMATHGoogle Scholar
  21. 21.
    National Recognition of Human Iris Patterns for Biometric Identification, http://people.csse.uwa.edu.au/pk/studentprojects/libor/LiborMasekThesis.pdf
  22. 22.
    Najafi, M., Ghofrani, S.: Iris Recognition Based on Using Ridgelet and Curvelet Transform. International Journal of Signal Processing, Image Processing and Pattern Recognition 4(2) (June 2011)Google Scholar
  23. 23.
    Daugman, J.: New methods in iris recognition. IEEE Trans. Systems, Man, Cybernetics B 37(5), 1167–1175 (2007)CrossRefGoogle Scholar
  24. 24.
    Mehrotra, H., Pankaj, K., Majhi, B.: Fast segmentation and adaptive SURF descriptor for iris recognition. Journal of Mathematical and Computer Modelling 58, 132–146 (2013)CrossRefGoogle Scholar
  25. 25.
    Belcher, C., Du, Y.: Region-based SIFT approach to iris recognition. Optics and Lasers in Engineering 47, 139–147 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • N. L. Manasa
    • 1
  • A. Govardhan
    • 1
  • Ch. Satyanarayana
    • 1
  1. 1.Jawaharlal Nehru Technological UniversityHyderabadIndia

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