中文摘要：

人脸识别中，表情、光照与遮挡变化引起的同类间的类内差异特征可在不同类间共享，为此，从已知样本数充足的样本库中可提取类内差异特征，从而达到扩充单样本训练库的目的。欠样本条件下扩展的稀疏表示人脸识别算法（Extended SRC，ESRC）利用类内图像相减，得到一个扩充的训练样本库，在一定程度上提高了单样本人脸识别率。但是，其扩充样本库的方法过于简单，样本库包含的特征信息有限。针对这点，本文引入联合稀疏模型（Jointly Sparse Model，JSM）提取类内差异特征，该模型将一连串相关联的信号表示成共同特征与差异特征之和，用该模型对样本数充足的人脸图像进行特征提取，把得到的类内差异特征与单样本一起作为稀疏表示识别算法的训练样本。基于 AR 人脸数据库的实验结果表明，该算法取得了较高的识别率，为单样本人脸识别问题提供了一个有效的解决途径。

英文摘要：

In the field of face recognition,intra-class difference features caused by expression,illumination and occlusion was sharable among other classes.Thus we could get intra-class difference features from face database where each kind of image is sufficient.By simple image subtracting,ESRC got an extra database to handle the single sample face recognition problem and got a good recognition rate.However,the intra-class difference features obtained by this method didn’t include all the difference between testing and training samples.To address this issue,we used JSMto obtain the intra-class difference information.A series of related signal could be represented as a combination of common features and discriminative features by JSM.Therefore,we could get discriminative features,namely intra-class difference features from sufficient samples.Finally training samples in SRC consisted of the single sample and the intra-class difference features as well.This algorithm gets a better result on AR face database and provides an effective solution for single sample face recognition problem.