Cauchy estimator discriminant analysis for face recognition

With the rapid development of computer vision and pattern recognition, face recognition, one of the basic research topics in computer vision and pattern recognition, has received intensive attention in recent years. Usually, traditional face recognition algorithms have considerable discriminant ability; however, when there are some samples that are easy to confuse in the face database, the discriminant ability of traditional face recognition algorithms will inevitably decrease. In this paper, based on the patch alignment framework (PAF) and Cauchy estimator theory, we proposed a novelty subspace learning algorithm for face recognition named Cauchy estimator discriminant analysis (CEDA). Under the framework of PAF, both local and global geometries of the input samples are preserved; by using the Cauchy estimator, large errors caused by samples that are easy to confuse could be overcome. We conducted the experiments on three face databases and strongly illustrated the effectiveness of CEDA for face recognition.