Group Sparse Representation Based on Feature Selection and Dictionary Optimization for Expression Recognition

被引：0
|
作者：
Xie H. [1 ,2 ]
Li M. [1 ,2 ]
Wang Y. [2 ]
Chen H. [1 ,2 ]
机构：
[1] School of Information Engineering, Nanchang Hangkong University, Nanchang
[2] Key Laboratory of Nondestructive Testing, Ministry of Education, Nanchang Hangkong University, Nanchang
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2021年 / 34卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Feature selection; Group sparse representation; Maximum scatter difference optimization learning; Small sample expression recognition;
D O I：
10.16451/j.cnki.issn1003-6059.202105007
中图分类号：
学科分类号：
摘要：
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. © 2021, Science Press. All right reserved.
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收藏
页码：446 / 454
页数：8
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