Discriminative low-rank dictionary learning for face recognition

Based on Low-Rank Representation (LRR), this paper presents a novel dictionary learning method to learn a discriminative dictionary which is more suitable for face recognition. Specifically, in order to make the dictionary more discriminating, we introduce an ideal regularization term with label information of training data to obtain low-rank coefficients. In the dictionary learning process, by optimizing the within-class reconstruction error and minimizing of between-class sub dictionaries, the learned dictionary has good representation ability for the training samples. In addition, we also suggest each sub dictionary is low-rank, which can violate with noise contained in training samples and make the dictionary more pure and compact. The learned dictionary and structured discriminative low-rank representation then will be used for classification. The proposed Discriminative Low-Rank Dictionary Learning (DLR_DL) method is evaluated on public face databases in comparison with previous dictionary learning under the same learning conditions. The experimental results demonstrate the effectiveness and robustness of our approach.