Fuzzy Kernel Fisher Discriminant Algorithm with Application to Face Recognition
Yujie Zheng, Jingyu Yang, Weidong Wang, Qiong Wang, Jian Jun Yang, Xiao‐Jun Wu · 2006
In this paper, a new kernel Fisher discriminant (KFD) algorithm with fuzzy set theory is studied. KFD algorithm is effective to extract nonlinear discriminative features of input samples with kernel trick. While conventional KFD algorithm assumes the same level of relevance of each sample to the corresponding class. In this paper, a novel KFD algorithm named fuzzy kernel Fisher discriminant (FKFD) is proposed. Distribution information of samples is represented with fuzzy membership degree in this paper. Furthermore, this information is utilized to redefine the corresponding scatter matrices, which are different to the conventional KFD algorithm and effective to extract discriminative features from overlapping (outlier) samples. Experimental results on ORL face database demonstrate the effectiveness of the proposed method