在逆Fisher鉴别分析的基础上，引入了模糊数学的思想，提出了模糊逆Fisher鉴别分析并成功应用于人脸识别。模糊逆Fisher鉴别分析通过隶属度函数将样本归入所有的类别之中，根据隶属度重新定义了类间散布矩阵和类内散布矩阵，进而将样本的原始分布信息通过相应的隶属度函数完全融入到了最后提取到的特征中。在ORL和FERET人脸库上的实验结果证明了基于模糊逆Fisher鉴别准则特征提取方法的优越性。

A new algorithm called fuzzy inverse fisher disciminant analysis is proposed. The inverse fisher discriminant analysis (IFDA) is effective in extracting discriminant features, but is assumed the same level of relevance of each sample to the corresponding class. Distribution information of samples is represented with fuzzy membership degree in the fuzzy inverse fisher discriminant analysis (FIFDA). Furthermore the information is utilized to redefine the corresponding scatter matrices, which are different from the IFDA. The experimental results on ORL and FERET face database indicate that the performance of FIFDA is superior to that of Fisherfaces and IFDA.