Regional approach for Iris Recognition

Recent studies have explored a various set of techniques, methods and algorithms for iris recognition. Segmentation algorithms, normalization processes and well-defined distances measures are employed as decision methods to compose the outline of all currently deployed systems, including commercial ones. So far, all systems make use of the entire iris pattern of any individual, assuming that a fixed threshold applied on the distance can partially avoid problems like noise of information and acquisition errors. In this paper a new approach is presented that uses only selected sectors of the iris and a simple analysis strategy, for verification purposed. Therefore, it differs from all previous approaches that process the entire iris pattern. In addition, three Bayes-based similarity measures have been considered for iris image matching: the Whitened Cosine (WC), the PRM Whitened Cosine Transform (PWC) and the Whitin-class Whitened Cosine Transform (WWC). The experimental results, carried out using the CASIA database, are encouraging and demonstrate that the proposed approach supports several directions for further research.