In this paper, we explore the applicability of first and second order monogenic Steerable Riesz wavelet components for iris recognition. These wavelets provide powerful mechanism to extract the invariant as well as covariant local variations of iris patterns. Unlike other existing methods where sole iris (either left or right) is used for recognition, in our work, we extract the features from both left and right irises, encode them separately and perform bit level fusion. Extensive experimentation has been conducted on the benchmark databases namely, IITD, MMU v-2 and CA-SIA v-4 distance to exhibit the performance of the proposed method. Comparative analysis is also performed with the state of the art methods to justify the suitability of the proposed method for iris recognition.

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ABSTRACT:Locating the boundary parameters of pupil and iris and segmenting the noise free iris portion are the most challenging phases of an automated iris recognition system. In this paper, we have presented person authentication frame work which uses particle swarm optimization (PSO) to locate iris region and circular hough transform (CHT) to device the boundary parameters. To undermine the effect of the noise presented in the segmented iris region we have divided the candidate region into N patches and used Fuzzy c-means clustering (FCM) to classify the patches into best iris region and not so best iris region (noisy region) based on the probability density function of each patch. Weighted mean Hammimng distance is adopted to find the dissimilarity score between the two candidate irises. We have used Log-Gabor, Riesz and Taylor's series expansion (TSE) filters and combinations of these three for iris feature extraction. To justify the feasibility of the proposed method, we experimented on the three publicly available data sets IITD, MMU v-2 and CASIA v-4 distance.

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