A Sparse Representation Method with Maximum Probability of Partial Ranking for Face Recognition

Abstract—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 successfully applied in face recognition. In this paper, we proposed a maximum probability of partial ranking method based on the framework of SRC, called SRC-MP, for face recognition. Eigenfiaces, fisherfaces, 2DPCA and 2DLDA are used for feature extraction. Experiments are implemented on two public face databases, Entended Yale B and ORL. In order to show our proposed method is robust for face recognition in the real world, experiment is also implemented on a web female album (WFA) face database. We utilize AdaBoost method to automatically detect human face from web album images with complex background, illumination variation and image misalignment to construct WFA database. Furthermore, we compare our proposed method with the classical projection-based methods such as principal component analysis (PCA), linear discriminant analysis (LDA), 2DPCA and 2DLDA. The experimental results demonstrate our proposed method not only is robust for varied viewing angles, expressions, and illumination, but also has higher recognition rates than other methods. Keywords-Compressive sensing; Face recognition; Sparse representation classification; AdaBoost