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Proceedings Paper

Robust iris recognition with region division
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Paper Abstract

In this paper, we propose an efficient and fast feature extraction method for iris recognition using wavelet transforms. The wavelet transforms have good space-frequency localization and there exist a number of fast algorithms. In particular, the coefficients of the lowest frequency band, reflecting the characteristics of the whole iris pattern, are used as a feature vector. However, a major problem of iris recognition is that noise such as the eyelid, the eyebrow and glint may be included in iris texture. Such noises adversely affect the performance of iris recognition systems. In order to solve these problems, after dividing the iris texture into a number of sub-regions, we propose to apply the wavelet transform separately to each sub-region and to extract a feature vector from each sub-region. In matching module, we discard some sub-regions which have large differences to exclude a potential noise. Experiments were performed using 3136 eye images acquired from 94 individuals. Experimental results show that the performance of proposed method is comparable to that of the method using Gabor transform and region division noticeably improves recognition performance for both methods. However, it is noted that the processing time of the former is much faster than that of the latter.

Paper Details

Date Published: 1 March 2005
PDF: 8 pages
Proc. SPIE 5672, Image Processing: Algorithms and Systems IV, (1 March 2005); doi: 10.1117/12.588038
Show Author Affiliations
Jonggeun Park, Yonsei Univ. (South Korea)
Chulhee Lee, Yonsei Univ. (South Korea)

Published in SPIE Proceedings Vol. 5672:
Image Processing: Algorithms and Systems IV
Edward R. Dougherty; Jaakko T. Astola; Karen O. Egiazarian, Editor(s)

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