Iris recognition using a low level of details

Authors:
Jaemin Kim;Seongwon Cho;Daewhan Kim;Sun-Tae Chung
Affiliations:
School of Electronics and Electrical Engineering, Hongik University, Seoul, Korea;School of Electronics and Electrical Engineering, Hongik University, Seoul, Korea;School of Electronics and Electrical Engineering, Hongik University, Seoul, Korea;School of Electronic Engineering, Soongsil University
Venue:
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Year:
2006

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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.