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Iris Recognition Using Gabor Filters
|關鍵字:||Iris recognition;虹膜辨識;Gabor filter;Feature selection;賈伯濾波器;特徵選取||出版社:||電機工程學系所||引用:|| F. H. Adler, Physiology of the Eye. St. Louis, Mo: Mosby, 1965.  L. Flom and A. Safir, “Iris recognition system,” U.S. Patent 4 641 349, 1987.  J. G. Daugman, “High confidence personal identification by rapid video analysis of iris texture,” Proc. IEEE Int. Carnahan Conf. on Security Technology, pp.1-11, 1992.  J. G. Daugman, “High confidence visual recognition of persons by a test of statistical independence,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1148-1161, Nov. 1993.  J. G. Daugman, “Biometric personal identification system based on iris analysis,” U.S. Patent 5 291 560, 1994.  J. G. Daugman, “Complete discrete 2-D Gabor transforms by neural network for image analysis and compression,” IEEE Trans. Acoust., Speech, Signal Processing, vol. 36, pp. 1169-1179, Jul. 1988.  J. G. Daugman, “New methods in iris recognition,” IEEE Trans. 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Iris recognition is an active and popular research topic in biometrics. A major challenge of the iris recognition is to extract the sufficient information from distinctive iris texture and to generate a compact iris code. In this dissertation, three iris feature extraction methods are proposed to build the different iris recognition systems. In the preprocessing stage, the lower part of the iris is unwrapped and normalized to a rectangular block. After that, the proposed feature extraction algorithms are applied to generate the iris feature vector.
In our first system, we adopt a bank of Gabor filters combined with the fractal dimension estimation to extract the iris features. The normalized iris block is first decomposed by the multi-channel Gabor filters. Then the differential box-counting method is used to estimate the fractal dimension of the filtered images. The Gabor decomposition is also adopted in the second proposed system in which the iris code is generated by analyzing and encoding the horizontal variation of each filtered image. Moreover, a sequential floating search scheme is used to discard the redundant features and improve the system performance in both first and second systems. In the third proposed system, the parameters of the Gabor filters are optimized by the particle swarm optimization. In addition, a sequential scheme is developed to determine the number of filters for a particular application. During the iris feature extraction, the normalized iris image is decomposed by the optimized Gabor filters and then a compact iris code is generated by using the relative variation analysis. Experimental results show that each proposed approach can produce comparable performance to existing methods.
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