A Kernel Two-Phase Test Sample Sparse Representation For Face Recognition

Sparse representation is a new hot technique in recent years. The two-phase test sample sparse representation method (TPTSSR) achieved an excellent performance in face recognition. In this paper, a kernel two-phase test sample sparse representation method (KTPTSSR) is proposed. Firstly, the input data are mapped into an implicit high-dimensional feature space by a non-linear mapping function. Secondly, the data are analyzed by means of the TPTSSR method in the feature space. If an appropriate kernel function and the corresponding kernel parameter are selected, a test sample can be accurately represented as the linear combination of the training data with the same label information of the test sample. Therefore, the proposed method could have better recognition performance than TPTSSR. Experiments on the face databases demonstrate the effectiveness of our methods.