Non-Orthogonal View Iris Recognition System.

This paper proposes a non-orthogonal view iris recognition system comprising a new iris imaging module, an iris segmentation module, an iris feature extraction module and a classification module. A dual-charge-coupled device camera was developed to capture four-spectral (red, green, blue, and nearinfrared) iris images which contain useful information for simplifying the iris segmentation task. An intelligent random sample consensus iris segmentation method is proposed to robustly detect iris boundaries in a four-spectral iris image. In order to match iris images acquired at different off-axis angles, we propose a circle rectification method to reduce the off-axis iris distortion. The rectification parameters are estimated using the detected elliptical pupillary boundary. Furthermore, we propose a novel iris descriptor which characterizes an iris pattern with multiscale step/ridge edge-type maps. The edge-type maps are extracted with the derivative of Gaussian and the Laplacian of Gaussian filters. The iris pattern classification is accomplished by edge-type matching which can be understood intuitively with the concept of classifier ensembles. Experimental results show that the equal error rate of our approach is only 0.04% when recognizing iris images acquired at different off-axis angles within ±30°. [ABSTRACT FROM AUTHOR]