Iris Recognition Using LVQ Neural Network

  • Seongwon Cho
  • Jaemin Kim
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


In this paper, we discuss human iris recognition, which is based on iris localization, feature extraction, and classification. The features for iris recognition are extracted from the segmented iris pattern using two-dimensional (2-D) wavelet transform based on Haar wavelet. We present an efficient initialization method of the weight vectors and a new method to determine the winner in LVQ neural network. The proposed methods have more accuracy than the conventional techniques.


Weight Vector Input Vector Classification Performance Iris Image Haar Wavelet 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Adler, F.H.: Physiology of the Eye: Clinical Application. C.V. Mosby Company (1965)Google Scholar
  2. 2.
    Hallinan, P.W.: Recognizing Human Eyes. In: SPIE Proc. of Geometric Methods in Computer Vision, vol. 1570, pp. 214–226 (1991)Google Scholar
  3. 3.
    Daugman, J.G.: High Confidence Visual Recognition of Persons by a Test of Statistical Independence. IEEE Trans. Pattern Analysis and Machine Intelligence 15(11), 1148–1161 (1993)CrossRefGoogle Scholar
  4. 4.
    Williams, G.O.: Iris Recognition Technology. IEEE AES Systems Magazine, 23–29 (1997)Google Scholar
  5. 5.
    Boles, W.W., Boashash, B.: A Human Identification Technique Using Images of the Iris and Wavelet Transform. IEEE Trans. Signal Processing 46(4), 1185–1188 (1998)CrossRefGoogle Scholar
  6. 6.
    Wildes, R.P.: Iris Recognition: An Emerging Biometric Technology. Proceedings of the IEEE 85(9), 1348–1363 (1997)CrossRefGoogle Scholar
  7. 7.
  8. 8.
    Cho, S., Seong, H.: Iris Recognition Using Gabor Transform and Neural Net. Journal of Fuzzy Logic and Intelligent Systems 7(2), 397–401 (1997)Google Scholar
  9. 9.
    Strang, G., Nguyen, T.: Wavelets and Filter Banks. Wellesley-Cambridge Press (1996)Google Scholar
  10. 10.
    Fausset, L.: Fundamentals of Neural Networks. Prentice Hall, Englewood Cliffs (1994)Google Scholar
  11. 11.
    Kohonen, T.: The Self-organization and Associate Memory. Springer, Heidelberg (1985)Google Scholar
  12. 12.
    Yin, F., Wang, J., Guo, C.: Advances in Neural Networks. Springer, Heidelberg (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Seongwon Cho
    • 1
  • Jaemin Kim
    • 1
  1. 1.School of Electronic and Electrical EngineeringHongik UniversitySeoulKorea

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