Locating the Visible Part of the Iris with a Texture Classifier with a Support Set

Abstract

Person identification by the iris is one of the leading technologies in biometric identification. The visible region of the iris has the form of a ring enclosed between the pupil and the sclera partially occluded by eyelids, eyelashes, and flashes. An important problem is to find the non-occluded part, i.e., divide the pixels of the image into two classes: “iris” and “occlusions.” We propose an approach to solving this problem based on distinguishing a support set, i.e., a part of the ring which is free from occlusions with high probability, and subsequently finding all elements that have similar texture features. As the support set, based on experiments we have chosen a sector of the ring with minimal brightness excess. We divide the pixels with a classifier based on a multidimensional Gaussian trained on the support set. Local classification noises are partially removed by morphological postprocessing. Applying this algorithm to construct biometric templates improves recognition.