IRIS RECOGNITION SYSTEM

Abstract

Iris Recognition is one of the most important biometric recognition systems to authenticate people. Iris recognition works by matching the iris patterns of two individuals. In this paper an efficient iris recognition system is proposed. The process used herein concentrates on image segmentation, normalization, feature extraction and encoding, and verification. Segmentation is done with the help of a great mix of Integro-Differential operator and Hough transformation. This approach provides you with better noise reduction and better iris deduction. Normalization of iris region is performed using the rubber sheet model of Daugman. Next, the properties of Gabor Filters are used to extract the features from the iris. Finally, matching of two templates or two iriscodes is performed by using the hamming distance formula to decide among imposters and genuine ones. The principle of operation used behind iris recognition system is the failure of a test of statistical independence and distinctiveness of the iris texture. This system gives better results than Libor Masek’s system, in terms of efficiency and in terms of time complexity. The proposed system gives better results than Libor Masek’s system, in terms of efficiency and time complexity. The proposed system performed more than 52900 comparisons for authentication on a set of 230 eye images. These images were taken from CASIA-IrisV4-Sync database.