Modifications and Improvements on Iris Recognition

Iris recognition is a well-known biometric technique. John Daugman has proposed a method for iris recognition, which is divided into four steps: segmentation, normalization, feature extraction and matching. In this paper, we evaluate, modify and extend John Daugman’s method. We study the images of CASIA and UBIRIS databases to establish some modifications and extensions on Daugman’s algorithm. The major modification is on the computationally demanding segmentation stage, for which we propose a template matching approach. The extensions on the algorithm address the important issue of pre-processing, that depends on the image database, being especially important when we have a non infra-red camera (e.g. a WebCam). For this typical scenario, we propose several methods for reflexion removal and pupil enhancement and isolation. The tests, carried out by our C# application on grayscale CASIA and UBIRIS images, show that our template matching based segmentation method is accurate and faster than the one proposed by Daugman. Our fast pre-processing algorithms efficiently remove reflections on images taken by non infra-red cameras.