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,Extended Yale B和CMU-PIE人脸库上进行实验,验证了文中方法的有效性及稳定性.
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.
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2016年第4期590-597,共8页
Journal of Computer-Aided Design & Computer Graphics
基金
国家科技支撑项目(2012BAH08B01)
江西省科技厅工业支撑重点项目(20151BBE50055)
江西省自然科学基金(20142BAB207007)
教育部人文社会科学研究青年基金(14YJCZH172)资助
关键词
稀疏表示分类
结构稀疏
结构贪婪算法
组合搜索
连通性
sparse representation-based classification
structural sparse
structural greedy algorithm
combinatorial search
connectivity