Iris Recognition Using a Low Level of Details

  • Jaemin Kim
  • Seongwon Cho
  • Daewhan Kim
  • Sun-Tae Chung
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4292)


This paper describes a new iris recognition algorithm, which uses a low level of details. Combining statistical classification and elastic boundary fitting, the iris is first localized. Then, the localized iris image is down-sampled by a factor of m, and filtered by a modified Laplacian kernel. Since the output of the Laplacian operator is sensitive to a small shift of the full-resolution iris image, the outputs of the Laplacian operator are computed for all space-shifts. The quantized output with maximum entropy is selected as the final feature representation. Experimentally we showed that the proposed method produces superb performance in iris segmentation and recognition.

Index Terms: iris segmentation, iris recognition, shift-invariant, multiscale Laplacian kernel.


Gaussian Mixture Model Iris Image Iris Recognition Iris Boundary Iris Pattern 
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. Mosby, St. Louis (1965)Google Scholar
  2. 2.
    Daugman, J.: High Confidence Visual Recognition of Person by a Test of Statistical Independence. IEEE Trans. Pattern Analysis and Machine Intelligence 15, 1148–1161 (1993)CrossRefGoogle Scholar
  3. 3.
    Wildes, R.P.: Iris Recognition: An Emerging Biometric Technology. Proceedings of the IEEE 85, 1348–1363 (1997)CrossRefGoogle Scholar
  4. 4.
    Zhu, Y., Tan, T., Wang, Y.: Biometric Person Identification based on Iris Pattern. In: ICPR 2000, pp. 805–808 (2000)Google Scholar
  5. 5.
    de Martin-Roche, D., Sanchez-Avila, C., Sanchez-Reillo, R.: Iris Recognition for Bio-metric Identification using Dyadic Wavelet Transform Zero-Crossing. In: Security Technology, 2001 IEEE 35th International Carnahan Conference, pp. 272–277 (2001)Google Scholar
  6. 6.
    Cho, S., Sung, H.I.: Wavelet transform and Competitive Learning Neural Network with Multi-dimensional Wiener Decision Strategy. In: Proceeding of KFIS Conference, vol. 8(2), pp. 341–345 (1998)Google Scholar
  7. 7.
    Lim, S., Lee, K., Byeon, O., Kim, T.: Efficient iris recognition through improved of feature vector and classifier. ETRI Journal 23(2), 61–70 (2001)CrossRefGoogle Scholar
  8. 8.
    Liang, J., Parks, T.W.: Image coding using translation invariant wavelet transforms with symmetric extensions. IEEE Trans. Image Processing 7(2) (1998)Google Scholar
  9. 9.
    Mallat, S., Zhong, S.: Characterization of signals from multiscale edges. IEEE Trans. Pattern Analysis and Machine Intelligence 14, 710–732 (1992)CrossRefGoogle Scholar
  10. 10.
    Simoncelli, E.P., Freeman, W.T., Adelson, E.H., Heeger, D.J.: Shiftable multiscale transforms. IEEE Trans. Inform. Theory 38, 587–607 (1992)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Boles, W.W., Boashah, B.: A Human Identification Technique Using Images of the Iris and Wavelet Transform. IEEE Trans. on Signal Processing 46, 1185–1188 (1998)CrossRefGoogle Scholar
  12. 12.
    Masahiko, S., Yuji, K.: Iris Region Extraction and Individual Identifying Device. Japan Patent, JP2000189403A2 (July 11, 2000)Google Scholar
  13. 13.
    Li, J., Barron, A.: Mixture Density Estimation. In: Solla, Leen, Mueller (eds.) Advances in Neural Information Processing Systems, vol. 12. The MIT Press, Cambridge (2000)Google Scholar
  14. 14.
    Lundervold, A., Storvik, P.: Segmentation of brain parenchyma and cerebrospinal fluid in multispectral magnetic resonance images. IEEE Trans. Medical Imaging 14, 339–349 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jaemin Kim
    • 1
  • Seongwon Cho
    • 1
  • Daewhan Kim
    • 1
  • Sun-Tae Chung
    • 2
  1. 1.School of Electronics and Electrical EngineeringHongik UniversitySeoulKorea
  2. 2.School of Electronic EngineeringSoongsil University 

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