Abstract
Iris recognition is a method used to identify people based on the analysis of the eye iris. A typical iris recognition system is composed of four phases: (1) image acquisition and preprocessing, (2) iris localization and extraction, (3) iris features characterization, and (4) comparison and matching. A novel contribution in the step of characterization of iris features is introduced by using a Hammersley’s sampling algorithm and accumulated histograms. Histograms are computed with data coming from sampled sub-images of iris. The optimal number and dimensions of samples is obtained by the simulated annealing algorithm. For the last step, couples of accumulated histograms iris samples are compared and a decision of acceptance is taken based on an experimental threshold. We tested our ideas with UBIRIS database; for clean eye iris databases we got excellent results.
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Castañon, L.E.G., de Oca, S.M., Morales-Menéndez, R. (2006). Optimal Sampling for Feature Extraction in Iris Recognition Systems. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_77
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DOI: https://doi.org/10.1007/11925231_77
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