KERNEL COLLABORATIVE REPRESENTATION-BASED CLASSIFIER FOR FACE RECOGNITION

Recent research has shown that collaborative representation-based classifier (CRC) can lead to promising results for the classification of face images. However, CRC is conducted in the original image space rather than the nonlinear high dimensional feature space in which features belonging to the same class are better grouped together and thus can be easily separable. To address this problem, this paper presents a novel classifier, Kernel Collaborative Representation-based Classifier (KCRC), by incorporating the kernel trick into the framework of CRC. Extensive experiments on both the AT&T and the FERET face databases demonstrate the priority of KCRC to CRC and several state-of-the-art methods.