中文摘要：

针对稀疏分类模型中存在着非连通的组结构, 为提高模型的表示能力, 提出一种非连通的动态组结构稀疏人脸识别方法. 该方法采用组合方式搜索所有可能的基块, 包括非连通基块, 通过基块的联合构造动态组结构; 将高维数据按类别分块, 在小的分块内进行组合搜索, 避免了组合爆炸; 采用编码复杂度来衡量数据的结构稀疏度,给出各种结构的编码复杂度计算方法; 基于结构贪婪算法实现非连通的动态组结构稀疏重构. 最后在AR, ExtendedYaleB 和CMU一PIE 人脸库上进行实验, 验证了文中方法的有效性及稳定性.

英文摘要：

Sparse representation-based classification model contains non-connected group structure. The proposed face recognition method utilizes non-connected dynamic group sparse to improve representation ability of model. The method employed combinatorial search to obtain all possible base-blocks, including non-con-nected base-blocks, and constructed dynamic group structure by union of the base-blocks. Meanwhile the method divided high dimensional data into blocks according to human category, and performed combinato-rial search within block in order to avoid combinatorial explosion. Furthermore the method adopted coding complexity to measure structural sparsity of coefficients, and analyzed calculation methods of coding com-plexity for several sparse structures; then achieved sparse reconstruction of non-connected dynamic group structure based on structured greedy algorithms. Finally, the face recognition experiments on the AR, Ex-tended YaleB and CMU-PIE databases demonstrate the effectiveness and stability of the proposed method.