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

摘要： 提出了分块二维主成分分析(分块2DPCA)的人脸识别方法。分块2DPCA方法先对图像矩阵进行分块,对分块得到的子图像矩阵直接进行鉴别分析。其特点是:能方便地降低鉴别特征的维数;可以完全避免使用矩阵的奇异值分解,特征抽取方便;与2DPCA方法相比,使用低维的鉴别特征矩阵,而达到较高(至少是不低)的正确识别率。此外,2DPCA是分块2DPCA的特例。在ORL和NUST603人脸库上的试验结果表明,所提出的方法在识别性能上优于2DPCA方法。

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