A novel face recognition method with nonlinear feature combination

A combined personalized feature framework is proposed for face recognition. In this framework, the novel linear discriminant analysis makes use of the space of the within-class scatter matrix effectively, and in order to simulate the recognition of the human visual system, global feature vectors and local feature vectors are integrated by complex vectors as input feature of linear discriminant analysis. The proposed method has been tested, in terms of classification error rate performance, on the multi-view UMIST face database. Results indicate that the proposed method is able to achieve excellent performance with only a small set of features.