Face Recognition Using Tensor Local Fisher Discriminant Analysis
张量局部Fisher判别分析的人脸识别

One of the key issues of face recognition is to extract the subspace features of face images. A new subspace dimensionality reduction method is proposed named as tensor local Fisher discriminant analysis (TLFDA), which benefits from two techniques, i.e., tensor based method and local Fisher discriminant analysis. Firstly, local Fisher discriminant analysis is improved for better recognition performance and reduced time complexity. Secondly, tensor based method employs two-sided transformation rather than single-sided one, and yields a higher compression ratio. Finally, TLFDA uses an iterative procedure to calculate the optimal solution of two transformation matrices. Experiment results on the ORL and PIE face databases show the effectiveness of the proposed method.