Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 3
889-894,918

Deep convolutional neural network fusing local feature and two-stage attention weight learning for facial expression recognition

Zheng Jian
Zheng Chi
Liu Hao
Yu Xiangchun
School of Information Engineering, Jiangxi University of Science & Technology, Ganzhou Jiangxi 341000, China
DOI: 10.19734/j.issn.1001-3695.2021.07.0287

Abstract

Facial local detail information plays an important role in facial expression recognition(FER). However, most of the existing methods only focus on the high-level semantic information of facial expressions, while ignoring the fine-grained information of local facial regions. To solve this problem, this paper proposed a deep convolutional neural network fusing local feature and two-stage attention weight learning(FLF-TAWL), which could adaptively capture important facial regions to improve the effectiveness of facial expression recognition. The FLF-TAWL model was composed of a dual-branch framework, one branch extracted local features from image blocks, and the other branch extracted global features from the entire expression image. Firstly, this paper proposed a two-stage attention weight learning strategy. In the first stage, it roughly learned the importance weights of global and local features, in the second stage, it further refined the attention weight, and fused the local and global features. Secondly, the model used a region-biased loss function to encourage the most important regions to obtain higher attention weights. Finally, this paper carried out extensive experiments on FERPlus, Cohn-Kanada(CK+) and JAFFE datasets to obtain accuracy rates of 90.92%, 98.90% and 97.39% respectively. The experimental results verify the effectiveness and feasibility of the FLF-TAWL model.

Foundation Support

国家自然科学基金资助项目(61563069,61462034)
江西省教育厅科学技术研究项目(GJJ170517,GJJ190468)
江西理工大学研究生创新专项资金资助项目(ZS2020-S049)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.07.0287
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 3
Section: Technology of Graphic & Image
Pages: 889-894,918
Serial Number: 1001-3695(2022)03-043-0889-06

Publish History

[2021-11-07] Accepted Paper
[2022-03-05] Printed Article

Cite This Article

郑剑, 郑炽, 刘豪, 等. 融合局部特征与两阶段注意力权重学习的面部表情识别 [J]. 计算机应用研究, 2022, 39 (3): 889-894,918. (Zheng Jian, Zheng Chi, Liu Hao, et al. Deep convolutional neural network fusing local feature and two-stage attention weight learning for facial expression recognition [J]. Application Research of Computers, 2022, 39 (3): 889-894,918. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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