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Proceedings Paper

Wavelet channel analysis of the multichannel iris recognition system and the improvement by wavelet packets
Author(s): De Cai; Qiaofeng Tan; Yingbai Yan; Guofan Jin; Qingsheng He
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Paper Abstract

Using iris feature, iris recognition attracts a lot of attention as a new and efficient personal identification technique in recent years. Compared with the frequently used methods of Daugman, Boles, et al., the dual multi-channel iris recognition system based on statistic features proposed by Yong Zhu, et al., has a unique and efficient algorithm. The algorithm processes gray iris image which is suitable to an Asian and takes good use of 2-D wavelet transformed irises. Moreover, they use statistic features to represent iris patterns which make their system more robust to errors caused in the image capturing stage. The recognition performance is better than the system of Wildes and approximates the system proposed by Daugman. But this system still has some open questions, such as, how wavelet filter channels influences the recognition and how to select wavelet channels. In this paper, we try to answer these questions. Via our analysis, it is proved that wavelet feature extraction can improve the identification rate and more wavelet filter channels results in better recognition. We also investigate the rule to choose the wavelet channels and conclude that high frequency channels are better than low frequency ones. Using this rule, we introduce wavelet packet channels to offer more useful information. The efficiency of this modification is shown by the experimental results.

Paper Details

Date Published: 2 November 2004
PDF: 8 pages
Proc. SPIE 5558, Applications of Digital Image Processing XXVII, (2 November 2004); doi: 10.1117/12.559651
Show Author Affiliations
De Cai, Tsinghua Univ. (China)
Qiaofeng Tan, Tsinghua Univ. (China)
Yingbai Yan, Tsinghua Univ. (China)
Guofan Jin, Tsinghua Univ. (China)
Qingsheng He, Tsinghua Univ. (China)


Published in SPIE Proceedings Vol. 5558:
Applications of Digital Image Processing XXVII
Andrew G. Tescher, Editor(s)

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