Robust Iris Segmentation Based on Local Image Gradient Properties

Abstract

Iris recognition is an important biometric method for personal identification. The accuracy of an iris recognition system highly depends on the success of an iris segmentation step. In this paper, a robust and accurate iris segmentation algorithm for closed-up NIR eye images is developed. The proposed method addressed problems of different characteristics of iris databases using local image properties. A precise pupil boundary is located with an adaptive thresholding combined with a gradient-based refinement approach. A new criteria, called a local signal-to-noise ratio (LSNR) of an edge map of an eye image is proposed for localization of the iris's outer boundary. The boundary is modeled with a weighted circular integral of LSNR optimization technique. The proposed method is experimented with multiple iris databases. The obtained results demonstrated that the proposed iris segmentation method is robust and desirable. The proposed method accurately segments iris region, excluding eyelids, eyelashes and light reflections against multiple iris databases without parameter tunings. The proposed iris segmentation method reduced false negative rate of the iris recognition system by half, compared to results obtained using Masek's method.