基于多核Fisher判别分析的人脸特征提取

Face Feature Extraction Based on Weighted Multiple Kernel Fisher Discriminant Analysis

核Fisher判别分析法是一种有效的非线性判别分析法。传统的核Fisher判别分析仅选用单个核函数,在人脸特征提取方面仍显不足。鉴于此,提出多核Fisher判别分析法,即通过将多个单核Fisher判别得到的投影进行加权组合得到加权投影,以加权投影为依据进行特征提取和分类。实验表明,在进行人脸特征提取和分类时,多核Fisher判别分析法优于单核Fisher判别分析法。

Kernel Fisher discriminant analysis method is an effective nonlinear discriminant analysis method.Tradition kernel Fisher discriminant analysis uses only single kernel function,which makes it insufficient in face feature extraction,therefore we proposed multiple kernel Fisher discriminant analysis.Weighted projections were obtained through weighted combination several projections obtained by single kernel Fisher discriminant,and then feature extraction and classification were made based on the weighted projections.Experimental results show that weighted multiple kernel Fisher discriminant analysis method is superior to single kernel Fisher discriminant analysis for facial feature extraction and classification.