中文摘要：

图像相似性先验嵌入的方法能有效提升基于稀疏编码表示的人脸识别在低维特征空间的识别性能．针对非受控人脸图像存在表情变化、部分遮挡和伪装的问题，提出基于图像分块的最大相似性嵌入稀疏编码表示的人脸识别方法．该方法首先将训练图像和测试图像进行同样的非重叠分块；然后计算测试图像与各训练图像对应分块间的相似性，并以其最大值度量图像间的相似性；最后将提取的最大块相似性信息嵌入到稀疏编码表示的人脸识别中．在AR标准人脸库上的测试表明，与全局相似性嵌入的加权稀疏编码表示分类方法相比，文中方法在训练样本和测试样本同时存在表情变化、遮挡和伪装的人脸识别中具有较大的性能提升．

英文摘要：

The performance of sparse representation based face recognition （SRFC） can be effectively improved by embedding a priori of similarity information. Aiming at expressions variations, partial occlusions and disguise in the uncontrolled face images, SRFC by embedding maximum block similarity information is proposed. Firstly, the training samples and query samples are divided into multiple non-overlapping blocks in the same way. Secondly, the similarities of corresponding blocks between the query samples and the training samples are calculated. Then, the maximum value is extracted to measure the similarity of inter-images. Finally, the extracted maximum block similarity information is embedded into sparse representation stage. Experimental results on AR face databases show that the proposed method achieves better recognition performance compared with those based on embedding global similarity, especially when both training images and query images contain expression, occlusions or disguise.