[1]王 彬,徐 杨*,石 进,等.多分支精简双线性池化的人脸表情识别[J].计算机技术与发展,2023,33(03):27-33.[doi:10. 3969 / j. issn. 1673-629X. 2023. 03. 005]
　WANG Bin,XU Yang*,SHI Jin,et al.Multi-branch Compact Bilinear Pooling for Facial Expression Recognition[J].,2023,33(03):27-33.[doi:10. 3969 / j. issn. 1673-629X. 2023. 03. 005]
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多分支精简双线性池化的人脸表情识别()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
33
期数:
2023年03期
页码:
27-33
栏目:
媒体计算
出版日期:
2023-03-10

文章信息/Info

Title:
Multi-branch Compact Bilinear Pooling for Facial Expression Recognition
文章编号:
1673-629X(2023)03-0027-07
作者:
王 彬1 ; 徐 杨1; 2* ; 石 进1 ; 张显国1
1. 贵州大学 大数据与信息工程学院,贵州 贵阳 550025;
2. 贵阳铝镁设计研究院有限公司,贵州 贵阳 550009
Author(s):
WANG Bin1 ; XU Yang1; 2* ; SHI Jin1 ; ZHANG Xian-guo1
1. School of Big Data and Information Engineering,Guizhou University,Guiyang 550025,China;
2. Guiyang Aluminum Magnesium Design and Research Institute Co. , Ltd. ,Guiyang 550009,China
关键词:
人脸表情识别; 多样化分支块; 残差空间注意力; 多分支精简双线性池化; ResNet-18
Keywords:
facial expression recognition; diverse branch block; residual spatial attention; multi-branch compact bilinear pool; ResNet-18
分类号:
TP391. 4
DOI:
10. 3969 / j. issn. 1673-629X. 2023. 03. 005
摘要:
针对人脸表情识别研究中特征提取不充分、难以辨别人脸表情细微的类间差异等问题,提出了一种多分支精简双线池性化的人脸表情识别方法。 该方法以 ResNet-18 为　基础,在避免大幅度增加计算复杂度的前提下提升 ResNet-18 的特征提取能力,提出了一个新的多样化分支块( diverse branch block) 对 ResNet-18 进行改进;为使改进后的 ResNet-18 更方便地聚焦人脸图像中产生表情区域的特征,提出了残差空间注意力;为了减少人脸表情细微的类间差异带来的不利影响,增强人脸表情类间的区别性,设计了多分支精简双线性池化结构。 最后用所提的方法分别在公开的人脸表情数据集CK+、RAF-DB 进行实验,识别率分别达到了 98. 46% 、82. 99% 。 实验结果表明,该方法的识别率优于 DLP-CNN、MA、DeepExp3D 等诸多的表情识别方法,具有一定的竞争性。
Abstract:
Aiming at the problems of insufficient feature extraction and difficulty in distinguishing subtle inter-class differences in facialexpression recognition in the study of facial expression recognition,a multi-branch compact bilinear pooling facial expression recognitionmethod is proposed. Based on ResNet-18,this method improves the feature extraction capability of ResNet-18 without greatly increasingthe computational complexity,and proposes a new diverse branch block to improve ResNet-18. To make the improved ResNet-18 moreconvenient to focus on the features of expression regions in face images,the residual spatial attention is proposed. In order to reduce the　adverse effects of subtle inter-class differences in facial expressions and enhance the distinction between facial expression classes,a multi-branch compact bilinear pooling structure is designed. Finally, the proposed method was used to conduct experiments on the publicfacial expression datasets CK+ and RAF-DB,and the recognition rates reached 98. 46% and 82. 99% ,respectively. The experimentalresults show that the recognition rate of the proposed method is better than that of DLP- CNN，MA, DeepExp3D and many otherexpression recognition methods,with certain competitiveness.

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更新日期/Last Update: 2023-03-10