IRIS RECOGNITION UNDER UNCONSTRAINED CONDITIONS

摘要

This paper studies the iris recognition under unconstrained conditions. In these circumstances iris recognition becomes challenging because of noisy factors such as the off-axis imaging, pose variation, image blurring, illumination change, occlusion, specular highlights and noise. A robust algorithm for localization of non-circular iris boundaries is proposed. It can localize the iris boundaries more accurately than the methods based on the Daugman algorithm. Operating on the filtered iris images, this method determines the outer iris boundaries. First we implemented Canny algorithm for edge detection in the segmented image. Then we ran the edge link algorithm on the edge map, achieving edge lists of connected edge points and selecting the longest one that has maximum number of points for outer iris boundary localization. Finally, we investigated how to extract highly distinctive features in the degraded iris images. We present a sequential forward selection method for seeking a sub-optimal subset of filters from a family of Gabor filters. The recognition performance is greatly improved with a very small number of filters selected. Experiments were conducted on the UBIRIS.v2 iris database and promising results were obtained.