包含非连通组的动态组结构稀疏人脸识别方法

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.%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 YaleB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。