  Despite the significant advances in iris segmentation, accomplishing accurate
iris segmentation in non-cooperative environment remains a grand challenge. In
this paper, we present a deep learning framework, referred to as Iris R-CNN, to
offer superior accuracy for iris segmentation. The proposed framework is
derived from Mask R-CNN, and several novel techniques are proposed 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 take into
account the iris and pupil circles to maximize the accuracy for iris
segmentation. Second, we propose a novel normalization scheme for Regions of
Interest (RoIs) to facilitate a radically new pooling operation over a
double-circle region. Experimental results on two challenging iris databases,
UBIRIS.v2 and MICHE, demonstrate the superior accuracy of the proposed approach
over other state-of-the-art methods.
