中文摘要：

在人脸识别中，每类数据分别位于由字典形成的高维空间中的多个低维线性子空间，考虑到这一结构信息对识别起到一定支持作用，利用块结构稀疏表示进行人脸识别。针对训练图像不能涵盖测试条件下的人脸变化这个问题，提出基于样本一扩展差分模板的联合双稀疏表示识别算法。它通过构造样本一扩展差分模板来表示训练样本与测试样本之间可能存在的差异，这些类内差异信息可被不同的类别所共享，即任何一类人脸图像的类内差异可表示为其他类别类内差异图像的原子稀疏线性组合。这样识别问题被转换为在训练样本空间和扩展差分模板空间寻找测试样本的块稀疏与原子稀疏的联合双稀疏表示。在AR和ExtendedYaleB数据库上的实验结果表明，在具有光照、表情变化和遮挡的情况下，本文提出的识别算法具有更好的有效性和鲁棒性。

英文摘要：

In the face recognition, data in each category lie in multiple low-dimensional subspaces of a high-dimensional space respectively. Because the structure information plays a certain support role, we apply the block-structured sparse rep- resentation for face recognition. Considering the problem that the training images can not span the facial variation under tes- ting conditions, a novel recognition method of joint bi-sparse representation based sample extended difference template is proposed, which applies an extended difference template to represent the possible variation between the training and testing images. These intra- category variation can be shared by other categories. In other words, the intra- category variation of any category can be represented as the atomic sparse linear combination. So the recognition problem is converted into finding a joint bi-sparse representation of the block-structured sparse representation and atomic sparse representation in the training sample space and extended difference template space. Experimental results on AR and Extended Yale B databases show that the proposed method has better effectiveness and robustness under variable expressions, illuminations and disguises.