Regularized Robust Sparse Representation Face Recognition Algorithm Based on Supervised Low-Rank Subspace Recovery

Due to training samples and query sample always filled with lighting and occlusion，then the image low-rank structure was destroyed. In view of this problem, a regularized robust sparse face representation algorithm which based on supervised low-rank subspace recovery is proposed. Firstly, the matrix D that is structured by all training samples is decomposed into low-rank classspecific structure A, low-rank non-class-specific structure B and sparse error structure E by the supervised low-rank decomposition. PCA is applied on the low-rank class-specific structure A to obtain the transform matrix; and then to project training samples and query samples onto low-rank subspace by using the transform matrix. Finally, utilizing weighted classification based on regularized robust sparse coding to classify the query image. Experimental results on AR and Extended Yale B face databases verify the effectiveness and robustness of our method.