Abstract
Iris Recognition (IR) under varying contrast conditions with low gradience is challenging, and exacting contrast invariant features is an effective approach to solve this problem. In this paper, we propose two novel techniques viz., Intensity Mapped Contrast Enhancement (IMCE) and Double symmetric rectangular hyperbolic based DFT (DsrhDFT) extraction. IMCE is a preprocessing technique used to increase the gradience between brighter and darker pixels of an image, thereby obtaining the salient iris features. DsrhDFT is used to extract prominent shift-invariant features, and a Binary Particle Swarm Optimization (BPSO) based feature selection algorithm is used to search the feature space for optimal feature subset. Individual stages of the IR system are examined and an attempt is made to improve each stage. Experimental results obtained by applying the proposed algorithm on Phoenix, MMU and IITD iris databases, show the promising performance of the IMCE+DsrhDFT for iris recognition. A significant increase in the recognition rate and a substantial reduction in the number of features is observed.
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Rakesh, S.M., Sandeep, G.S.P., Manikantan, K., Ramachandran, S. (2013). DFT-Based Feature Extraction and Intensity Mapped Contrast Enhancement for Enhanced Iris Recognition. In: S, M., Kumar, S. (eds) Proceedings of the Fourth International Conference on Signal and Image Processing 2012 (ICSIP 2012). Lecture Notes in Electrical Engineering, vol 221. Springer, India. https://doi.org/10.1007/978-81-322-0997-3_43
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DOI: https://doi.org/10.1007/978-81-322-0997-3_43
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