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ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India- Vol I pp 167–177Cite as

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

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Part of the book series: Advances in Intelligent Systems and Computing ((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].

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References

  1. Global biometrics market revenue to reach $20 billion by 2018, http://www.biometricupdate.com

  2. Daugman, J.: How Iris Recognition Works. IEEE Trans. on Circuits & Systems for Video Technology 14(1) (January 2004)

    Google Scholar 

  3. Iris identification solutions, http://www.neurotechnology.com/verieye-technology.html

  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. 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. 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. 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. 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)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  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. 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. 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)

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  14. CASIA IrisV3 Description document, http://www.idealtest.org/findTotalDbByMode.do?mode=Iris

  15. Selesnick, I.W.: Hilbert Transform Pairs of Wavelet Bases. IEEE Sig. Proc. Letters (2001)

    Google Scholar 

  16. DTCW, http://eeweb.poly.edu/iselesni/WaveletSoftware/dt2D.html

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  19. CASIA Image Iris database, http://www.idealtest.org/findTotalDbByMode.do?mode=Iris

  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)

    Article  MATH  Google Scholar 

  21. National Recognition of Human Iris Patterns for Biometric Identification, http://people.csse.uwa.edu.au/pk/studentprojects/libor/LiborMasekThesis.pdf

  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. Daugman, J.: New methods in iris recognition. IEEE Trans. Systems, Man, Cybernetics B 37(5), 1167–1175 (2007)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  25. Belcher, C., Du, Y.: Region-based SIFT approach to iris recognition. Optics and Lasers in Engineering 47, 139–147 (2009)

    Article  Google Scholar 

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Authors and Affiliations

  1. Jawaharlal Nehru Technological University, Hyderabad, Andhra Pradesh, India

    N. L. Manasa, A. Govardhan & Ch. Satyanarayana

Authors
  1. N. L. Manasa
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  2. A. Govardhan
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  3. Ch. Satyanarayana
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Corresponding author

Correspondence to N. L. Manasa .

Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences, Vishakapatnam, India

    Suresh Chandra Satapathy

  2. College of Engineering(A), Andhra University, Vishakapatnam, India

    P. S. Avadhani

  3. University of Hyderabad, Hyderabad, India

    Siba K. Udgata

  4. CSIR-National Institute of Oceanography, Visakhapatnam, India

    Sadasivuni Lakshminarayana

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© 2014 Springer International Publishing Switzerland

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Manasa, N.L., Govardhan, A., Satyanarayana, C. (2014). Fusion of Dual-Tree Complex Wavelets and Local Binary Patterns for Iris Recognition. In: Satapathy, S., Avadhani, P., Udgata, S., Lakshminarayana, S. (eds) ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India- Vol I. Advances in Intelligent Systems and Computing, vol 248. Springer, Cham. https://doi.org/10.1007/978-3-319-03107-1_20

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  • DOI: https://doi.org/10.1007/978-3-319-03107-1_20

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03106-4

  • Online ISBN: 978-3-319-03107-1

  • eBook Packages: EngineeringEngineering (R0)

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