Face recognition by sparse discriminant analysis via joint L2,1-norm minimization

Recently, joint feature selection and subspace learning, which can perform feature selection and subspace learning simultaneously, is proposed and has encouraging ability on face recognition. In the literature, a framework of utilizing L-2,L-1-norm penalty term has also been presented, but some important algorithms cannot be covered, such as Fisher Linear Discriminant Analysis and Sparse Discriminant Analysis. Therefore, in this paper, we add L-2,L-1-norm penalty term on FLDA and propose a feasible solution by transforming its nonlinear model into linear regression type. In addition, we modify the optimization model of SDA by replacing elastic net with L-2,L-1-norm penalty term and present its optimization method. Experiments on three standard face databases illustrate FLDA and SDA via L-2,L-1-norm penalty term can significantly improve their recognition performance, and obtain inspiring results with low computation cost and for low-dimension feature. (C) 2014 Elsevier Ltd. All rights reserved.