A sparse representation method with maximum probability of partial ranking for face recognition

Face recognition is a popular topic in computer vision applications. Compressive sensing is a novel sampling technique for finding sparse solutions to underdetermined linear systems. Recently, a sparse representation-based classification (SRC) method based on compressive sensing is presented. It has been shown to be robust for face recognition. In this paper, we proposed a maximum probability of partial ranking method based on the framework of SRC, called SRC-MP. It computes the maximum probability from the largest λ weighting coefficients for the individuals, respectively. Experiments are implemented on Extended Yale B and ORL face databases using eigenfaces, fisherfaces, 2DPCA and 2DLDA for feature extraction. Furthermore, we compare our proposed method with classical projection-based methods such as principal component analysis (PCA), linear discriminant analysis (LDA), 2DPCA and 2DLDA. The experimental results demonstrate our proposed method is able to achieve higher recognition rate than other methods.