模糊的类内矩阵模式主分量分析及在人脸识别中的应用

矩阵模式主分量分析（MatPCA）作为有效的特征提取方法能同时处理矩阵表式的模式和向量表式的模式。但与主分量分析（PCA）方法一样，MatPCA没有使用样本的类别信息，因此所提取的特征不能提供足够的判别信息，进而影响随后的分类性能。为有效利用样本的类别信息，在MatPCA基础上提出了一种新的特征提取方法——模糊的类内MatPCA（F—WMatPCA）。F—WMatPCA利用模糊K最近邻（FKNN）求解训练样本的模糊隶属度并在模糊的类内执行MatPCA。由于F—WMatPCA使用更多的类别信息，因此能有效地提高识别精度。对0RL，Yale人脸数据集和几个UCI数据集做了实验，结果证实了该方法的有效性。另外，讨论了F—WMatPCA在人脸识别上的应用，并与F—Fisherfaces作了比较，结果表明F—WMatPCA具有比F—Fisherfaces更稳定、更鲁棒的识别性能。

FUZZY WITHIN-CLASS MATRIX PRINCIPAL COMPONENT ANALYSIS AND ITS APPLICATION TO FACE RECOGNITION

Matrix principal component analysis (MatPCA),as an effective feature extraction method,can dealwith the matrix pattern and the vector pattern.However,like PCA,MatPCA does not use the class informationof samples.As a result,the extracted features cannot provide enough useful information for distinguishing pat-tern from one another,and further resulting in degradation of classification performance.To fullly use class in-formation of samples,a novel method,called the fuzzy within-class MatPCA (F-WMatPCA)is proposed.F-WMatPCA utilizes the fuzzy K-nearest neighbor method (FKNN) to fuzzily the class membership degrees of atraining sample and then performs fuzzy MatPCA within these patterns having the same class label.Due to moreclass information is used in feature extraction,F-WMatPCA can intuitively improve the classification perfor-mance.Experimental results in face databases and some benchmark datasets show that F-WMatPCA is effectiveand competitive than MatPCA.The experimental analysis on face image databases indicates that F-WMatPCA im-proves the recognition accuracy and is more stable and robust in performing classification than the existing methodof fuzzy-based F-Fisherfaces.