Abstract—Biometrics deals with identification of individuals based on their biological or behavioral characteristics. Iris recognition is one of the robust biometric technologies used for personal identification. Iris recognition

Biometrics deals with identification of individuals based on their biological or behavioral characteristics. Iris recognition is one of the robust biometric technologies used for personal identification. Iris recognition includes image acquisition, segmentation, normalization, feature extraction and matching. Firstly, an iris image is acquired from CASIA database. Segmentation is used for the localization of the correct iris region in an eye and it should be done accurately and correctly to remove the eyelids, eyelashes, reflection and pupil noises present in iris region. This paper proposes Hough transform segmentation method for iris recognition. The segmented iris is normalized to minimize the dimensional inconsistencies between iris regions by using Daugman’s Rubber Sheet Model. For extracting iris features, one-dimensional object detection is used. Finally, the two iris textures are matched using Euclidean distance. The proposed system provides accurate iris segmentation and reduces the computation and time load extracting texture information of the iris. Keywords—Daugman’s Rubber Sheet Model, Euclidean Distance, Hough transform, Iris Recognition.