关键词: 线性鉴别分析, 特征抽取, 分块二维主成分分析, 特征矩阵, 人脸识别

Abstract: A human face recognition technique based on modular 2DPCA was presented. First, the original images were divided into modular images in proposed approach. Then the 2DPCA method could be directly used to the sub-images obtained from the previous step. There are three advantages for this way: 1)dimension reduction of discriminant features can be done conveniently; 2)singular value decomposition of matrix is fully avoided in the process of feature extraction, so the features for recognition can be gained easily; 3)as opposed to 2DPCA, the feature matrix of lower dimension can be employed, and higher (not less at least) correct recognition rate can be reached. Moreover, 2DPCA is the special case of modular 2DPCA. To test modular 2DPCA and evaluate its performance, a series of experiments were performed on three human face image databases: ORL and NJUST603 human face databases. The experimental results indicated that the performance of modular 2DPCA is superior to that of 2DPCA.

Key words: LDA(Linear Discriminant Analysis), feature extraction, Modular 2DPCA (Modular two-Dimensional Principal Component Analysis), feature matrix, face recognition