Experimental analysis regarding the influence of iris segmentation on the recognition rate

In this paper we will look at the detection and segmentation of the iris and its influence on the overall performance of the iris-biometric tool chain. We will examine whether the segmentation accuracy, based on conformance with a ground truth, can serve as a predictor for the overall performance of the iris-biometric tool chain. That is: If the segmentation accuracy is improved will this always improve the overall performance? Furthermore, we will systematically evaluate the influence of segmentation parameters, pupillary and limbic boundary and normalization center (based on Daugman’s rubbersheet model), on the rest of the iris-biometric tool chain. We will investigate if accurately finding these parameters is important and how consistency, i.e., extracting the same exact region of the iris during segmenting, influences the overall performance.