Discriminative Dictionary Learning with Low-Rank Regularization for Face Recognition

We consider learning a discriminative dictionary in sparse representation and specifically focus on face recognition application to improve its performance. This paper presents an algorithm to learn a discriminative dictionary with low- rank regularization on the dictionary. To make the dictionary more discerning, we apply Fisher discriminant function to the coding coefficients with the goal that they have a small ratio of the within- class scatter to between- class scatter. However, noise in the training samples will undermine the discrimination power of the dictionary. To handle this problem, we base on low- rank matrix recovery theory and apply a low- rank regularization on the dictionary. The proposed discriminative dictionary learning with low- rank regularization ( D-2 (LR2)-R-2) algorithm is evaluated on several face image datasets in comparison with existing representative dictionary learning and classification algorithms. The experimental results demonstrate its superiority.