Parity symmetrical collaborative representation-based classification for face recognition

Although the subspace-based feature extraction algorithms provided a feasible strategy to deal with the classification of high-dimensional data, most of the existing algorithms are locality-oriented and suffer from many difficulties such as uncertain information associated with dataset and small sample size problem. In this paper, we propose a novel collaborative representation-based classification method using parity symmetry strategy for face recognition. More specifically, we firstly synthesize a set of parity symmetrical images by means of odd–even decomposition theorem, aiming to augment the training set. Secondly, each query sample is represented as a linear combination of the training samples from the extended training set, we then exploit the optimal representation of each reconstructed image with relevant contribution from each class. The final goal of the proposed method is to generate the best parity symmetrical representation of the query sample to perform robust face classification. Experimental results conducted on ORL, FERET, AR, PIE and LFW face databases demonstrate the effectiveness of the proposed method.