Abstract
Biometric refers to a science for analyzing the human characteristics such as physiological or behavioral patterns. Iris is a physiological trait, which is unique among all the biometric traits to recognize an individual effectively. In this paper, MSB based iris recognition based on Discrete Wavelet Transform, Independent Component Analysis and Binarized Statistical Image Features is proposed. The left and right region is extracted from eye images using morphological operations. Binary split is performed to divide the eight-bit binary of every pixel into four bit Least Significant Bits and four bit Most Significant Bits. The DWT is applied on four bit MSB to extract the iris features. Then ICA is applied on approximate sub band to extract the significant details of iris. The obtained features are then applied on BSIF to obtain the enhanced response with final features. Finally, generated features are then matched with test features using Euclidean distance classifier on CASIA v1.0 database to analyse the proposed iris model.
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Harakannanavar, S.S., Prashanth, C.R., Raja, K.B. (2020). MSB Based Iris Recognition Using Multiple Feature Descriptors. In: Pandian, A., Ntalianis, K., Palanisamy, R. (eds) Intelligent Computing, Information and Control Systems. ICICCS 2019. Advances in Intelligent Systems and Computing, vol 1039. Springer, Cham. https://doi.org/10.1007/978-3-030-30465-2_68
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DOI: https://doi.org/10.1007/978-3-030-30465-2_68
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