Abstract
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.
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References
Adler, F.H.: Physiology of the Eye. Mosby, St. Louis (1965)
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)
Wildes, R.P.: Iris Recognition: An Emerging Biometric Technology. Proceedings of the IEEE 85, 1348–1363 (1997)
Zhu, Y., Tan, T., Wang, Y.: Biometric Person Identification based on Iris Pattern. In: ICPR 2000, pp. 805–808 (2000)
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)
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)
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)
Liang, J., Parks, T.W.: Image coding using translation invariant wavelet transforms with symmetric extensions. IEEE Trans. Image Processing 7(2) (1998)
Mallat, S., Zhong, S.: Characterization of signals from multiscale edges. IEEE Trans. Pattern Analysis and Machine Intelligence 14, 710–732 (1992)
Simoncelli, E.P., Freeman, W.T., Adelson, E.H., Heeger, D.J.: Shiftable multiscale transforms. IEEE Trans. Inform. Theory 38, 587–607 (1992)
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)
Masahiko, S., Yuji, K.: Iris Region Extraction and Individual Identifying Device. Japan Patent, JP2000189403A2 (July 11, 2000)
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)
Lundervold, A., Storvik, P.: Segmentation of brain parenchyma and cerebrospinal fluid in multispectral magnetic resonance images. IEEE Trans. Medical Imaging 14, 339–349 (1995)
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Kim, J., Cho, S., Kim, D., Chung, ST. (2006). Iris Recognition Using a Low Level of Details. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2006. Lecture Notes in Computer Science, vol 4292. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11919629_21
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DOI: https://doi.org/10.1007/11919629_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-48626-8
Online ISBN: 978-3-540-48627-5
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