Abstract
Iris is one of the most discriminative biometric trait because it has random discriminating texture which does not change much, over a long time period. They are unique for all individuals, even for twins and the left and right eyes of the same individuals. In this paper an iris recognition system is presented that does iris segmentation, normalization, segregating of unwanted parts like occlusion, specular reflection and noise. Later iris images are enhanced and feature extraction and matching is performed. Iris features are extracted using Discrete Cosine Transform (DCT) and Relational Measure (RM). Later fusion of the dissimilarity scores of two feature extraction techniques has been proposed to get better performance. The results have been shown on large publicly available databases like CASIA-4.0 Interval, Lamp and self-collected IITK. The proposed fusion have achieved encouraging results.
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Nigam, A., Kumar, B., Triyar, J., Gupta, P. (2015). Iris Recognition Using Discrete Cosine Transform and Relational Measures. In: Azzopardi, G., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science(), vol 9257. Springer, Cham. https://doi.org/10.1007/978-3-319-23117-4_44
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DOI: https://doi.org/10.1007/978-3-319-23117-4_44
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