Comparison and a neural network approach for iris localization

Abstract

Abstract This paper presents a fast and reliable approach for the localization of an iris image using neural networks by comparing some of the existing localization methods. Normally any biometric recognition system is used for identification of an individual and verified with the available database to check whether the person is authorized or not. Nowadays, iris recognition systems are considered the best authentication method compared to other biometric systems due to the unique characteristic feature of the human iris. In iris recognition systems, the important and difficult step is to locate or segment the iris from the input eye image which is responsible for success rate of the iris recognition. Hence this paper suggests a efficient approach to locate the iris using neural networks in order to improve the efficiency of recognition systems. Further, the paper compares the existing iris localization methods such as Daugman algorithm, Hough transform and Canny edge detector algorithm. The best localization algorithm is chosen for training a network and simulating for efficient and fast segmentation of irises with good success rate.