Sparse learning for salient facial feature description

High dimension of the features employed for face recognition is the main reason to slow down the recognition speed. Additionally, selecting salient facial features has significant impact on the efficiency of face recognition. In order to get the sparse and salient facial features, this paper propose a new sparse learning approach for salient facial feature description. This approach is to learn the feature evaluation vector with the training samples composed of within- and between-class distance vector sets. Then, the feature evaluation vector is employed to construct a new model for salient facial feature description. Experimental results show that the proposed method achieves much better face recognition performance with lower feature dimensionality.