A New Discriminative Collaborative Neighbor Representation Method for Robust Face Recognition

As the representative one of representation-based classification (RBC) methods, collaborative RBC (CRC) has drawn much attention in pattern recognition and machine learning recently. Moreover, the collaborative representation-based face recognition has been extensively studied because of the effective classification performance of CRC. CRC collaboratively represents each query sample as the linear combination of all the training samples and then classifies the query sample according to the categorical representation-based distances. However, most variants of CRC cannot fully consider the locality and discrimination of data and cannot well handle the noise data, which has negative effect on real-world classification problems, such as face recognition. In this paper, a new discriminative collaborative neighbor representation (DCNR) method for face recognition is proposed by integrating class discrimination and data locality. In the proposed method, the locality of data constrains collaborative representation of each query sample to find representative nearest samples of the query sample. Moreover, the class discrimination regularization is taken into account by employing the representation of each class for each query sample. Due to the existing noises, such as corruptions and occlusions in face recognition, we further propose robust DCNR (R-DCNR) for robust classification by using the l(1)-norm representation fidelity. Extensive experiments on face databases demonstrate that the proposed methods achieve competitive classification performance, compared to the state-of-the-art representation-based classification methods.