Robust iris recognition using sparse error correction model and discriminative dictionary learning

Robust iris recognition is a hot research topic in the biometrics community and the sparse representation-based methods are promising to achieve desirable robustness and accuracy. Motivated by the fact that corruptions and occlusions incurred by eyelash occlusions, eyelid overlapping, specular and cast reflection in iris images are spatially localized but large in magnitude, we present a robust iris recognition method based on a sparse error correction model. In the proposed method, all the training images are concatenated as a dictionary and the iris recognition task is cast to an optimization problem to seek a sparse representation of the test sample in terms of the dictionary. And a sparse error correction term is introduced into the objective function of the optimization problem to deal with gross and spatially localized errors. Furthermore, in order to compact the huge dictionary, we introduce a discriminative dictionary learning framework to reduce computational complexity. Experimental results on CASIA Iris Image Database V3.0 show that the proposed methods achieve competitive performance in both recognition accuracy and efficiency.