Robust discriminative nonnegative dictionary learning for occluded face recognition

Face recognition in real-world video surveillance needs to deal with a lot of challenges including low resolution, illumination variations, pose changes, occlusions and so on. Among them, occlusions are difficult and have not attracted enough attentions. To address this problem, in this paper, we propose a robust discriminative nonnegative dictionary learning method for occluded face recognition, which estimates the occlusions adaptively and selects the features robustly. Instead of modeling the reconstruction errors using a specific distribution, we estimate occlusions adaptively according to the reconstruction errors and learn different weights for different pixels during the iterative processing. To enhance discriminant ability of the dictionary, we constrain the low-dimensional representations of samples from the same class to be as close as possible and select the discriminative features robustly via ℓ2, 1-norm. For the induced non-convex problem, we reformulate it into local convex optimization subproblem via utilizing the half-quadratic technique and propose new update rules. Extensive experiments are implemented on four benchmark datasets, and the experimental results demonstrate the effectiveness and robustness of the proposed method.