Robust and efficient iris recognition based on sparse error correction model

Abstract

Iris recognition has become one of the most promising approaches for biometric authentication. Due to the fact that corruption and occlusion in iris images caused by eyelash occlusion, eyelid overlapping, specular and cast reflection is large in magnitude but sparse in spatial, a sparse representation method based on sparse error correction model is introduced in the paper. To improve the robustness and efficiency of the recognition system, each iris sample is separated into a few sectors, and a Bayesian fusion-based cumulative SCI (CSCI) approach is applied to validate the recognition results. Experimental results on CASIA-IrisV3 demonstrate the proposed method achieves excellent recognition performance both in robustness and efficiency. © 2013 Springer-Verlag.