Human Identification by Iris Recognition using Artificial Bee Colony Algorithm

Abstract

The iris which is an internal part of the human eye can be used for human identification. An iris recognition system uses template matching to compare two iris images and generate a match score that reflects their degree of similarity or dissimilarity. Eye apart from iris has useless parts such as eyelid and eyelash. The iris is taken as the region of interest by masking the useless parts and is done by Active Contour Algorithm (ACA). The texture feature in the iris is extracted using Independent Component Analysis (ICA). Artificial Bee Colony (ABC) algorithm which is an evolutionary algorithm is then used to select the best feature from the extracted texture features. By using Hamming Distance the best feature is then compared with the several features of different individuals in the database for identification. For proving the effectiveness and feasibility; we compare the proposed specific feature selection approach with the method without feature selection on a small database. The experimental results show the proposed approach can achieve lower error rates in iris authentication.