Face Recognition Combing Principal Component Analysis and Fractional-step Linear Discriminant analysis

A new feature extraction approach for face recognition that combines principal component analysis and fractional-step linear discriminant analysis is proposed in this paper. In order to reduce the computational complexity of the algorithm, principal component analysis is first used to reduce the dimension. In addition, before using F-LDA, we transform the pooled within-class scatter matrix into an identity matrix. The new approach is tested on AR and UMIST face databases. Experiment results show that this algorithm gains higher classification accuracy than other existing methods