Facial Expression Recognition Using Multi-Branch Attention Convolutional Neural Network
Facial expression recognition technology was extensively used. This paper develops a multi-branch attention convolutional neural network based on a multiple-branch structure to recognize facial expressions. First, features are extracted from facial images in a multi-branch architecture, and features...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10004585/ |
| _version_ | 1797959607794008064 |
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| author | Yinggang He |
| author_facet | Yinggang He |
| author_sort | Yinggang He |
| collection | DOAJ |
| description | Facial expression recognition technology was extensively used. This paper develops a multi-branch attention convolutional neural network based on a multiple-branch structure to recognize facial expressions. First, features are extracted from facial images in a multi-branch architecture, and features from three branches are fused. Second, to address the issue of insufficient feature extraction and poor recognition performance, the Convolutional Block Attention Module is added as attention module. Third, our model reduces parameters and computation loads by using depth-wise separable convolutions. The experiments on the FER2013, FERPLUS, and CK+ datasets show that the recognition rates of the proposed model are 69.49%, 84.633%, and 99.39%, respectively. The proposed method has a higher efficiency in extracting image features than traditional deep learning counterparts and achieves high accuracy without complicated artificial feature technology. |
| first_indexed | 2024-04-11T00:35:08Z |
| format | Article |
| id | doaj.art-9f2c2d59768c4a77acbe3407a2f307e4 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T00:35:08Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-9f2c2d59768c4a77acbe3407a2f307e42023-01-07T00:00:51ZengIEEEIEEE Access2169-35362023-01-01111244125310.1109/ACCESS.2022.323336210004585Facial Expression Recognition Using Multi-Branch Attention Convolutional Neural NetworkYinggang He0https://orcid.org/0000-0002-1532-2956Chengyi College, Jimei University, Xiamen, ChinaFacial expression recognition technology was extensively used. This paper develops a multi-branch attention convolutional neural network based on a multiple-branch structure to recognize facial expressions. First, features are extracted from facial images in a multi-branch architecture, and features from three branches are fused. Second, to address the issue of insufficient feature extraction and poor recognition performance, the Convolutional Block Attention Module is added as attention module. Third, our model reduces parameters and computation loads by using depth-wise separable convolutions. The experiments on the FER2013, FERPLUS, and CK+ datasets show that the recognition rates of the proposed model are 69.49%, 84.633%, and 99.39%, respectively. The proposed method has a higher efficiency in extracting image features than traditional deep learning counterparts and achieves high accuracy without complicated artificial feature technology.https://ieeexplore.ieee.org/document/10004585/Facial expression recognitionmulti-branch architecturedepth-wise separable convolutionsconvolutional block attention module |
| spellingShingle | Yinggang He Facial Expression Recognition Using Multi-Branch Attention Convolutional Neural Network IEEE Access Facial expression recognition multi-branch architecture depth-wise separable convolutions convolutional block attention module |
| title | Facial Expression Recognition Using Multi-Branch Attention Convolutional Neural Network |
| title_full | Facial Expression Recognition Using Multi-Branch Attention Convolutional Neural Network |
| title_fullStr | Facial Expression Recognition Using Multi-Branch Attention Convolutional Neural Network |
| title_full_unstemmed | Facial Expression Recognition Using Multi-Branch Attention Convolutional Neural Network |
| title_short | Facial Expression Recognition Using Multi-Branch Attention Convolutional Neural Network |
| title_sort | facial expression recognition using multi branch attention convolutional neural network |
| topic | Facial expression recognition multi-branch architecture depth-wise separable convolutions convolutional block attention module |
| url | https://ieeexplore.ieee.org/document/10004585/ |
| work_keys_str_mv | AT yingganghe facialexpressionrecognitionusingmultibranchattentionconvolutionalneuralnetwork |