Group Sparse Representation Based on Feature Selection and Dictionary Optimization for Expression Recognition
XIE Huihua1,2, LI Ming1,2, WANG Yan2, CHEN Hao1,2
1. School of Information Engineering, Nanchang Hangkong University, Nanchang 330063; 2. Key Laboratory of Nondestructive Testing, Ministry of Education, Nanchang Hangkong University, Nanchang 330063
Abstract：To solve the over-fitting problem of recognition model on small sample facial expression database, a group sparse representation classification method based on feature selection and dictionary optimization is put forward. Firstly, the feature selection criterion is proposed, and the complementary features of same class-level sparse mode and different intra-class sparse mode are selected to build a dictionary. Then, the dictionary is learned by maximum scatter difference optimization to reconstruct features without distortion and acquire a high discriminative ability. Finally, the optimized dictionary is combined for group sparse representation classification. Experiments on JAFFE and CK+ databases show that the proposed method is robust to sample reduction with high generalization ability and recognition accuracy.
谢惠华, 黎明, 王艳, 陈昊. 基于特征优选和字典优化的组稀疏表示表情识别[J]. 模式识别与人工智能, 2021, 34(5): 446-454.
XIE Huihua, LI Ming, WANG Yan, CHEN Hao. Group Sparse Representation Based on Feature Selection and Dictionary Optimization for Expression Recognition. , 2021, 34(5): 446-454.
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2021, Vol. 34 
