An Effective Segmentation Method for Iris Recognition System

Iris recognition has become a popular research in recent years due to its reliability and nearly perfect recognition rates. Iris recognition system has three main stages: image preprocessing, feature extraction and template matching. In the preprocessing stage, iris segmentation is critical to the success of subsequent feature extraction and template matching stages. If the iris region is not correctly segmented, the eyelids, eyelashes, reflection and pupil noises would present in the normalized iris region. The presence of noises will directly deteriorate the iris recognition accuracy. The proposed approach gives a solution for compensating all four types of noises to achieve higher accuracy rate. It consists of four parts: (a) Pupil is localized using thresholding and Circular Hough Transform methods. (b) Two search regions including the outer iris boundaries are defined to locate the outer iris. (c) Two search regions are selected based on pupil position to detect the upper and lower eyelids. (d) Thresholding is implemented to remove eyelashes, reflection and pupil noises. The method evaluates on iris images taken from the CASIA iris image database version 1.0 [1]. Experimental results show that the proposed approach has achieved high accuracy of 98.62%.