Linear discriminant analysis based on weighted Fisher criteria and face recognition
基于加权Fisher准则的线性鉴别分析及人脸识别

A novel method based on weighted discriminant analysis for face recognition was proposed in this paper. First, the Fisher criterion was redefined by introducing a weighting of the contributions of individual class pairs to the overall criterion. Then, to deal with the high dimensional and singular case in face recognition problems, a simple and efficient algorithm was developed. Finally, the proposed algorithm was tested on ORL face database, and a recognition rate of 96% was achieved by using either a common nearest neighbor classifier or a minimum distance classifier. The experimental results show our method is superior to the classical Eigeafaces and Fisherfaces.