关键词: 稀疏表示, 核方法, 谱回归, 流形学习, 人脸识别

Abstract: In view of the low classification accuracy and high computational complexity of traditional kernel sparse representation classification methods on high dimensional data sets, a kernel sparse representation classification method based on spectral regression is proposed. Firstly, spectral regression analysis is used to obtain the transformation matrix for feature extraction, and the sample data are extracted by the transformation matrix. Then the kernel method will project the features to high dimensional feature space to make it more separability. Finally the use of kernel method in high dimensional feature space enables us to use the sparse representation method to identify the face images. By combining the spectral regression method with the kernel sparse representation classification method, the manifold structure and class information of the data set are effectively utilized, which can solve the problem of sparse representation classification of high dimension face images. Experimental results on the standard face image data sets show that the proposed method not only improves the recognition rate, but also reduces the time cost of the algorithm. It can be applied in the classification of high dimensional face image data effectively.

Key words: sparse representation based classification, kernel method, spectral regression, manifold learning, face recognition