Abstract
We present a new approach for iris recognition based on a sampling scheme. Iris recognition is a method to identify persons, based on the analysis of the eye iris. A typical iris recognition system is composed of four phases: image acquisition and preprocessing, iris localization and extraction, iris features characterization, and comparison and matching. The main contribution in this work is in the step of characterization of iris features by using sampling methods and accumulated histograms. These histograms are built from data coming from sampled subimages of iris. In the comparison and matching step, a comparison is made between accumulated histograms of couples of iris samples, and a decision of accept/reject is taken based on samples differences and on a threshold calculated experimentally. We tested two sampling methods: random and Hammersley, and conduct experiments with UBIRIS database. Under certain conditions we found a rate of successful identifications in the order of 100 %.
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Garza Castañón, L.E., de Oca, S.M., Morales-Menéndez, R. (2006). An Application of Random and Hammersley Sampling Methods to Iris Recognition. In: Ali, M., Dapoigny, R. (eds) Advances in Applied Artificial Intelligence. IEA/AIE 2006. Lecture Notes in Computer Science(), vol 4031. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11779568_56
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DOI: https://doi.org/10.1007/11779568_56
Publisher Name: Springer, Berlin, Heidelberg
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