Obtaining Stable Iris Codes Exploiting Low-Rank Tensor Space and Spatial Structure Aware Refinement for Better Iris Recognition

The strength of iris recognition in terms of optimal biometric performance has been challenged by inevitable operational conditions in unconstrained scenarios. In this work we present a new approach for extracting stable iris weight maps to account for the noisy iris representation as a result of capture conditions and ineluctable segmentation errors. Traditional approaches to extract stable bits often ignore inter-code relations under the presence of multiple enrolment samples. Unlike previous works, we formulate the stable code extraction using tensor representation to exactly recover the low-rank non-noisy iris information using the multiple enrolment samples. Further, the proposed approach produces stable class specific (user specific) iris weight maps by eliminating the error bits due to sub-optimal segmentation or pupil dilation effects using spatial correspondence in a patch-wise manner. Through the set of experiments on two publicly available iris databases acquired under semi-constrained and unconstrained setting, we demonstrate the superiority for identification and verification performance over current state-ofthe-art algorithms. Rank−1 identification rate on CASIAv4 distance database is achieved at 93.3% and a verification accuracy of Genuine Match Rate (GMR) of 80% at False Match Rate(FMR) of 0.0001 indicating the applicability of proposed approach in operational scenarios.