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Báo cáo hóa học: "Research Article Iris Recognition for Partially Occluded Images: Methodology and Sensitivity Analysis"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Iris Recognition for Partially Occluded Images: Methodology and Sensitivity Analysis | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 36751 12 pages doi 10.1155 2007 36751 Research Article Iris Recognition for Partially Occluded Images Methodology and Sensitivity Analysis A. Poursaberi1 and B. N. Araabi1 2 1 Department of Electrical and Computer Engineering Control and Intelligent Processing Center of Excellence Faculty of Engineering University of Tehran P.O. Box 14395-515 Tehran Iran 2 School of Cognitive Sciences Institute for Studies in Theoretical Physics and Mathematics P.O. Box 19395-5746 Tehran Iran Received 17 March 2005 Revised 12 January 2006 Accepted 15 March 2006 Recommended by Wilfried Philips Accurate iris detection is a crucial part of an iris recognition system. One of the main issues in iris segmentation is coping with occlusion that happens due to eyelids and eyelashes. In the literature some various methods have been suggested to solve the occlusion problem. In this paper two different segmentations of iris are presented. In the first algorithm a circle is located around the pupil with an appropriate diameter. The iris area encircled by the circular boundary is used for recognition purposes then. In the second method again a circle is located around the pupil with a larger diameter. This time however only the lower part of the encircled iris area is utilized for individual recognition. Wavelet-based texture features are used in the process. Hamming and harmonic mean distance classifiers are exploited as a mixed classifier in suggested algorithm. It is observed that relying on a smaller but more reliable part of the iris though reducing the net amount of information improves the overall performance. Experimental results on CASIA database show that our method has a promising performance with an accuracy of 99.31 . The sensitivity of the proposed method is analyzed versus contrast illumination and noise as well where lower sensitivity to all factors is observed when the lower .