Semantic segmentation of color eye images for improving iris segmentation

Abstract

Iris segmentation under visible spectrum (VIS) is a topic that has been gaining attention in many researches in the last years, due to an increasing interest in iris recognition at-a-distance and in non-cooperative environments such as: blur, off-axis, occlusions, specular reflections among others. In this paper, we propose a new approach to detect the iris region on eye images acquired under VIS. We introduce the semantic information of different classes of an eye image (such as sclera, pupil, iris, eyebrows among others) in order to segment the iris region. Experimental results on UBIRIS v2 database show that the semantic segmentation improves the iris segmentation by reducing the intra-class variability, especially for the non-iris classes.