Automated Segmentation of Iris Images Using Visible Wavelength Face Images

Abstract

Remote human identification using iris biometrics requires the development of automated algorithm of the robust segmentation of iris region pixels from visible face images. This paper presents a new automated iris segmentation framework for iris images acquired at-a-distance using visible imaging. The proposed approach achieves the segmentation of iris region pixels in two stages, i.e. (i) iris and sclera classification, and (ii) post-classification processing. Unlike the traditional edge-based segmentation approaches, the proposed approach simultaneously exploits the discriminative color features and localized Zernike moments to perform pixel-based classification. Rigorous experimental results presented in this paper confirm the usefulness of the proposed approach and achieve improvement of 42.4% in the average segmentation errors, on UBIRIS.v2 dataset, as compared to the previous approach.