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包含非连通组的动态组结构稀疏人脸识别方法

Face Recognition Method Using Non-Connected Dynamic Group Sparse

  • 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.

     

    Abstract: 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-connected 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 combinatorial 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 complexity 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, Extended Yale B and CMU-PIE databases demonstrate the effectiveness and stability of the proposed method.

     

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