中文摘要：

针对训练样本字典学习仅包含全局信息、缺乏局部信息的不足,引入与类别相关的原子字典,提出基于原子与分子字典联合扩展的加权稀疏表示人脸识别方法。首先,对各类训练样本进行PCA学习,得到带标记的训练样本基,构造PCA基原子字典,同时将训练样本字典作为分子字典。进而,利用原子字典与分子字典结合得到扩展字典模型。测试时,根据测试样本与扩展字典基之间的距离进行加权得到与当前测试样本关联的重构字典集,最后对测试样本稀疏重构,利用残差进行分类判别。为验证本文方法有效性,分别在AR、Georgia Tech和CMU PIE人脸数据库上进行实验。

英文摘要：

In view of the ordinary training sample dictionary learning only contains the global information in SRC,we introduce the atom dictionary which is related categories,and propose atomic and molecular dictionary joint extended weighted sparse representation of the human face recognition algorithm. Firstly the labeled atom dictionary is learned by each kind of training samples with PCA respectively,and at the same time the molecule dictionary is learned from training samples. So the extended dictionary is obtained by the combination of atom dictionary and molecule dictionary. When the input is tested,computing the weight according to the distance between the test sample and extended dictionary,so we can get the restructured dictionary associated with the current test sample. Finally,the test sample is classified by using of residual error SRC criterion. In order to verify the effectiveness of our algorithm,we conduct experiments respectively in the AR,Georgia Tech and CMU PIE facial database.