Multiple kernel locality-constrained collaborative representation-based discriminant projection for face recognition

Collaborative representation-based classifier (CRC) has achieved superior classification performance in the field of face recognition. However, the performance of CRC will degrade significantly when facing non-linear structural data. To address this problem, many kernel CRC (KCRC) methods have been proposed. These methods usually use a predetermined kernel function which is difficult to be selected. In addition, how to select appropriate parameters remains a challenging problem. Hence, multiple kernel technology (MKL) is applied on CRC, which called MK-CRC. However, it only considers the representation errors while ignoring the class label information in the training process. In this paper, we propose a multiple kernel locality-constrained collaborative representation-based classifier (MKLCRC) which is the multiple kernel extension of CRC and considers the local structures of data. Based on the classification rule of MKLCRC, we propose a dimensionality reduction (DR) method called multiple kernel locality-constrained collaborative representation-based discriminant projection (MKLCR-DP). The goal of MKLCR-DP is to learn a projection matrix and a set of kernel weights to generate a low-dimensional subspace where the between-class reconstruction errors are maximized and the within-class reconstruction errors are minimized. Thus MKLCRC can achieve better performance in this low-dimensional subspace. The proposed method can be efficiently optimized with the trace ratio optimization. Experiments on AR, extended Yale B, FERET, CMU PIE and LFW face databases demonstrate that our method outperforms related state-of-the-art algorithms.