基于Fisher 判别分析的加权估计纹理分析.

Traditional texture analysis methods mark global similarity only by related attribution of each face area. For the issue that global information is represented by local information which causes bad feature extracting, weighting estimation for texture analysis (WETA) based on Fisher discriminative analysis (FDA) is proposed. Firstly, face images are divided into some non-overlapping local patches with same sizes after texture coding by using local binary patterns (LBP) or local phase quantization (LPQ). The solution is given by the most discriminative axis within a similarity space using Fisher discriminative analysis and weight optimization after extracting coordinate axes with the most discrimination. Finally, the efficiency of proposed method is verified by experiments conducted on the FERET and on the FEI face databases. The experiments indicate that the proposed method brings a better recognition performance in comparison to other weighting methods proposed in the literature. [ABSTRACT FROM AUTHOR]