Face recognition algorithm based on LRC and collaborative representation

By representing the input testing image as a sparse linear combination of the training samples sparse representation based classification (SRC) has shown promising results for face recognition (FR). We consider the problem of low efiectiveness and eficiency of SRC algorithm when the face database is huge. Combining linear regression classification (LRC) with collaborative representation, we propose a coarse-to-fine FR algorithm, namely accelerated linear collaborative representation based classification (ALCRC) algorithm. The proposed algorithm contains two stages. In the first stage, we use LRC to coarsely select the candidate training set according to least residuals. Thus the search space of the training set is reduced. In the second stage, we employ collaborative representation based classification (CRC) to make the proposed algorithm robust to face occlusion and variations such as illuminations, expressions and poses. Experiments are done on the AR, ORL and FERET databases, and results demonstrate that the proposed algorithm has higher recognition rates and eficiency in comparison to SRC and CRC, and it is more robust than SRC and CRC. ©, 2014, Journal of Computational Information Systems. All right reserved.