Face recognition based on sparse representation and error correction SVM
Jing Wang, Chengan Guo · 2012
Very recently, the sparse representation theory in pattern recognition has aroused widespread concern. It shows that a sample can be linearly recovered by the others in the database and the coefficients are sparse. Based on this theory, this paper proposed a new feature extraction algorithm-Sparse Representation Discrimination Analysis (SRDA) by combining the sparse representation theory and the manifold learning model together. The SRDA algorithm can maintain not only the sparse reconstruction relationship of original data, but also the spatial structure in low dimensional space. Then, the SRDA feature is integrated with the error correction SVM to build a new face recognition system. Comparative experiments of various face recognition approaches are conducted by testing on the ORL, AR and FERET databases in the paper and the experimental results show the superiority of the new method.