Fast Iris Segmentation Algorithm for Visible Wavelength Images Based on Multi-color Space

Abstract

Iris recognition for eye images acquired in visible wavelength is receiving increasing attention. In visible wavelength environments, there are many factors that may cover or affect the iris region which makes the iris segmentation step more difficult and challenging. In this paper, we propose a novel and fast segmentation algorithm to deal with eye images acquired in visible wavelength environments by considering the color information form multiple color spaces. The various existing color spaces such as RGB, YCbCr, and HSV are analyzed and an appropriate set of color models is selected for the segmentation process. To accurately localize the iris region, a set of convenient techniques are applied to detect and remove the non-iris regions such as pupil, specular reflection, eyelids, and eyelashes. Our experimental results and comparative analysis using the UBIRIS v2 database demonstrate the efficiency of our approach in terms of segmentation accuracy and execution time.