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Iris Feature Extraction using Independent Component Analysis  

노승인 (연세대학교 전기전자공학과)
배광혁 (연세대학교 전기전자공학과)
박강령 (상명대학교 미디어학부)
김재희 (연세대학교 전기전자공학과)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.40, no.6, 2003 , pp. 20-30 More about this Journal
Abstract
In a conventional method based on quadrature 2D Gator wavelets to extract iris features, the iris recognition is performed by a 256-byte iris code, which is computed by applying the Gabor wavelets to a given area of the iris. However, there is a code redundancy because the iris code is generated by basis functions without considering the characteristics of the iris texture. Therefore, the size of the iris code is increased unnecessarily. In this paper, we propose a new feature extraction algorithm based on the ICA (Independent Component Analysis) for a compact iris code. We implemented the ICA to generate optimal basis functions which could represent iris signals efficiently. In practice the coefficients of the ICA expansions are used as feature vectors. Then iris feature vectors are encoded into the iris code for storing and comparing an individual's iris patterns. Additionally, we introduce two methods to enhance the recognition performance of the ICA. The first is to reorganize the ICA bases and the second is to use a different ICA bases set. Experimental results show that our proposed method has a similar EER (Equal Error Rate) as a conventional method based on the Gator wavelets, and the iris code size of our proposed methods is four times smaller than that of the Gabor wavelets.
Keywords
iris recognition; biometrics; independent component analysis; feature extraction;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
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