Face Recognition Using Enhanced Fisher Linear Discriminant Model with Facial Combined Feature

Achieving higher classification rate under various conditions is a challenging problem in face recognition community. This paper presents a combined feature Fisher classifier (CF2C) approach for face recognition, which is robust to moderate changes of illumination, pose and facial expression. The success of this method lies in that it uses both facial global and local information for robust face representation while at the same time employs an enhanced Fisher linear discriminant model (EFM) for good generalization. Experiments on ORL and Yale face databases show that the proposed approach is superior to traditional methods, such as eigenfaces and fisherfaces.