International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Iris Recognition Based on a Shift-Invariant Wavelet Transform

  • Cho, Seongwon (School of Electronics and Electrical Engineering, Hongik University) ;
  • Kim, Jaemin (School of Electronics and Electrical Engineering, Hongik University)
  • Published : 2004.12.01
Download PDF
⟨ Previous Next ⟩

Abstract

This paper describes a new iris recognition method based on a shift-invariant wavelet sub-images. For the feature representation, we first preprocess an iris image for the compensation of the variation of the iris and for the easy implementation of the wavelet transform. Then, we decompose the preprocessed iris image into multiple subband images using a shift-invariant wavelet transform. For feature representation, we select a set of subband images, which have rich information for the classification of various iris patterns and robust to noises. In order to reduce the size of the feature vector, we quantize. each pixel of subband images using the Lloyd-Max quantization method Each feature element is represented by one of quantization levels, and a set of these feature element is the feature vector. When the quantization is very coarse, the quantized level does not have much information about the image pixel value. Therefore, we define a new similarity measure based on mutual information between two features. With this similarity measure, the size of the feature vector can be reduced without much degradation of performance. Experimentally, we show that the proposed method produced superb performance in iris recognition.

Keywords

References

  1. J. Daugman, 'High Confidence Visual Recognition of Person by a Test of Statistical Independence,' IEEE Trans. Pattern Analysis and Machine Intelligence, vol. IS, pp. 1148-1161, 1993
  2. J. Daugrnan, 'The Importance of being random: statistical principles of iris recognition,' Pattern Recognition, vol. 36, no. 2, pp. 297-291, 2003
  3. R. P. Wildes, 'Iris Recognition: An Emerging Biometric Technology,' Proceedings of the IEEE, vol. 85, pp.1348-1363, 1997 https://doi.org/10.1109/5.628669
  4. Y. Zhu, T. Tanlnd Y. Wang, 'Biometric Person Identification based on Iris Pattern,' ICPR2000, pp.805-808, 2000
  5. D. de Martin-Roche, C. Sanchez-Avila, and R. Sanchez-Reillo, 'Iris Recognition for Biometric Identification using Dyadic Wavelet Transform ZeroCrossing,' Security Technology, 2001 IEEE 35th International Carnahan Conference, pp. 272-277, 2001
  6. S. Cho and H.Sung, 'Wavelet transform and Competitive Learning Neural Network with Multi-dimensional Wiener Decision Strategy,' Proceeding of KFIS Conference, vol. 8, No.2, pp. 341-345, 1998
  7. S. Mallat and S. Zhong, 'Characterization of signals from multiscale edges,' IEEE Trans. Pattern Analysis and Machine Intelligence,Vol. 14, pp 710-732, July 1992 https://doi.org/10.1109/34.142909
  8. E. P. Simoncelli, W. T. Freeman, E.H. Adelson, and D. J. Heeger, 'Shiftable multiscale transforms,' IEEE Trans. Inform. Theory, vol. 38, pp 587-607, Mar. 1992 https://doi.org/10.1109/18.119725
  9. J. Liang and T. W. Parks, 'Image coding using translation invariant wavelet transforms with symmetric extensions,' IEEE Tons. Image Processing, vol. 7, No.2 1998
  10. W. W. Boles, and B. Boashah, 'A Human Identification Technique Using Images of the Iris and Wavelet Transform,' IEEE Trans. on Signal Processing, vol. 46, pp. 1185-1188, 1998 https://doi.org/10.1109/78.668573
  11. J. S. Lim, Two-dimensional signal and image processing, Prentice Hall PTR, 1989
  12. J. Kittler and M. Hatef, R. P.W. Duin, and J. Matas, 'On Combining Classifiers,' IEEE Trans. Pattern Analysis and Machine Intelligence,Vol. 20, pp 226-239, March 1998 https://doi.org/10.1109/34.667881