An efficient iris recognition system based on Modular Neural Networks

In this paper, we propose a neural network based iris recognition approach by analyzing iris patterns. The iris recognition system consists of iris localization, feature extraction and classification of the iris images. Hough transforms were used for localizing the iris region; Cartesian to polar coordinate transform was used for transforming the ring shaped iris image to the rectangular shape. Then, histogram equalization was applied to the iris image for making the shapes in image more distinctive. Average absolute deviation (AAD) algorithm was used for feature extraction in this approach. In matching process, Multi-Layered Perceptron (MLP) and Modular Neural Networks (MNN) are applied to the iris feature vector for classifying the iris images. In fact, this research is focused on measuring the performance of MNN in iris recognition system compared with Multi-Layered Perceptron (MLP) neural network. The gray-level iris images, experimented in this work, were obtained from Institute of Automation Chinese Academy of Science (CASIA) iris images database and Departments of Informatics University of Beira Interior (UBIRIS) iris images database. Experimental results are given in the last stage of this paper.