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中文摘要：
提出了模块2DPCA（two-dimensional principal component analysis）的人脸识别方法。模块2DPCA方法先对图像矩阵进行分块，将分块得到的子图像矩阵直接用于构造总体散布矩阵，然后利用总体散布矩阵的特征向量进行图像特征抽取。与基于图像向量的鉴别方法（比如PCA）相比，该方法在特征抽取之前不需要将子图像矩阵转化为图像向量，能快速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；此外，模块2DPCA是2DPCA的推广。在ORL和NUST603人脸库上的试验结果表明，模块2DPCA方法在识别性能上优于PCA，比2DPCA更具有鲁棒性。
英文摘要：
A human face recognition technique called modular 2DPCA is presented in this paper. First, the original images are divided into modular images in proposed approach. Then, an image covariance matrix is constructed directly using the sub-images, and its eigenvectors are derived for image feature extraction. Compared with previous techniques based on image vectors such as PCA, there are two advantages for this way： 1 ） the sub-image matrices don＇ t need to be transformed into vectors prior to feature extraction, and dimension reduction of discriminant features can be effected conveniently; 2） singular value decomposition of matrix is absolutely avoided in the process of feature extraction so the features for recognition can be gained easily. Moreover, 2DPCA is the special case of modular 2DPCA. To test modular 2DPCA and evaluate its performance, a series of experiments were performed on two human face image databases： ORL and NJUST603 human face databases. The experimental results indicated that the recognition performance of modular 2DPCA is superior to that of PCA and is more robust than that of 2DPCA as well.
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