融合2DPCA和模糊2DLDA的人脸识别

摘要

A new face image feature extraction method which combined fuzzy set theory and Two-Dimentional TwoDimentional Principal Component Analysis -Linear Discriminant Analysis ((2D) 2 PCALDA) was proposed.Firstly, TwoDimentional Principal Component Analysis (2DPCA) was used to extract the optimal projective vectors from face image.Then,the membership degree matrix was calculated by fuzzy K- nearest neighbor algorithm, and it was merged into the process of Two-Dimentional Linear Discriminant Analysis (2DLDA).Finally, fuzziness between-class scatter matrix and fuzziness withinclass scatter matrix were obtained.Compared with (2D) 2 PCALDA, the method made full use of the advantages of (2D)2 PCALDA.It not only effectively extracted the row and column recognition information, but also made full use of the distribution information of samples.The experiments based on Yale and FERET face databases show that the method can have better recognition effect than (2D)2PCALDA and (2D)2PCA.%结合模糊集理论、双向二维主成分-线性鉴别分析((2D)2PCALDA)的特点,提出一种新的人脸图像特征提取方法.算法首先对人脸图像进行二维主成分分析(2DPCA)处理,再用模糊K近邻算法计算图像的隶属度矩阵,并将其融入到2DLDA过程中,从而得到模糊类间散射矩阵和模糊类内散射矩阵.与(2D)2PCALDA相比,该算法充分利用了(2D)2PCALDA的优点,有效地提取了行和列的识别信息,并充分考虑了样本的分布信息.在Yale和FERET人脸数据库上的实验结果表明,该方法识别效果优于(2D)2PCALDA、双向二维主成分分析((2D)2PCA)等方法.