针对单样本人脸识别问题，提出了权重模块2DPCA识别方法。该方法首先利用模块2DPCA方法对图像矩阵进行区域分块和子图像主成分特征提取，再用光流方法度量测试图像和样本图像对应分块像素区域由于人物变化、表情不同、饰物遮蔽等造成的差异，并以此为依据对得到的样本和测试图像的特征矩阵之间的差分矩阵分块区域赋以相对权重，最后进行最邻近分类判别。在JAFFE和ORL人脸库上的实验结果表明，在同等鉴别特征维数下，权重模块2DPCA识别方法较之传统2DPCA方法和模块2DPCA方法具有更高的识别率和鲁棒性,证明了在基于PCA的人脸识别方法中加入先验知识以提高识别能力的可行性。

In view of face recognition with only on sample problem, we propose a weighted modular 2DPCA method in this paper. In the method, we first divide original images into modular images and accomplish the sub image 2DPCA feature extraction. Then, we use optical flow between testing and sample image to estimate difference of corresponding pixel blocks quantitatively, which is as criterion for us to give variant weights to each block of difference matrixes between the feature matrixes of sample and that of probing images. Finally, nearest neighbor classifier is employed for classification. The experiment results on the JAFFE and ORL human face database indicate that weighted modular 2DPCA is superior to both conventional 2DPCA and modular 2DPCA in terms of accuracy and robustness with the same dimension of discriminate features, and it is feasible to introduce prior knowledge into PCA method of face recognition.