Iris R-CNN: Accurate iris segmentation and localization in non-cooperative environment with visible illumination

Abstract

Despite the significant advances in iris segmentation, accomplishing accurate iris segmentation and localization in non-cooperative environment with visible illumination remains a grand challenge. In this paper, we present an end-to-end multi-task deep neural network, referred to as Iris R-CNN, to achieve superior accuracy for iris segmentation and localization. Inspired by instance segmentation, Iris R-CNN seamlessly integrates segmentation and localization in a unified framework and generates a normalized iris image/mask required for iris recognition. It is composed of several novel techniques to carefully explore the unique characteristics of iris. First, we propose two novel networks, (i) Double-Circle Region Proposal Network (DC-RPN) and (ii) Double-Circle Classification and Regression Network (DC-CRN), to efficiently capture pupil and iris circles and enhance the accuracy for iris localization. Second, we propose a novel normalization scheme for Regions of Interest (RoIs) to enable a radically new pooling operation over a double-circle region. To facilitate accurate training and validation, we annotate two public datasets in non-cooperative environment with visible illumination: NICE-II and MICHE. Experimental results on these two challenging datasets demonstrate the superior accuracy of our proposed approach over other state-of-the-art methods.