Facial Expression Recognition with Multi-perspective Feature Fusion Under Deep Residual Convolution
Aiming at the problems of inaccurate facial expression recognition and large amount of calculation under multi-perspective in real life, a facial expression recognition model MVResNet-FER is proposed, which is based on multi-perspective feature fusion under deep residual convolution.The residual...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Harbin University of Science and Technology Publications
2023-04-01
|
| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2203 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849716275454935040 |
|---|---|
| author | GUAN Xiaorui GAO Lu SONG Wenbo LIN Kezheng |
| author_facet | GUAN Xiaorui GAO Lu SONG Wenbo LIN Kezheng |
| author_sort | GUAN Xiaorui |
| collection | DOAJ |
| description |
Aiming at the problems of inaccurate facial expression recognition and large amount of calculation under multi-perspective in real life, a facial expression recognition model MVResNet-FER is proposed, which is based on multi-perspective feature fusion under deep residual convolution.The residual block in ResNet is first improved and the conventional convolutional network is replaced with a depthwise separable network.Second, a CBAM module is added to enhance the extraction of effective features under multi-perspective and the supplementation of shallow feature information. Then use the RReLu activation function to replace the original ReLu to avoid deactivation of some nodes when the gradient is large.Finally, the global average pooling layer is used instead of the fully connected layer to achieve dimensionality reduction, and the generated feature vector is sent to Softmax for classification Experiments show that the proposed method produces excellent results on the CK+ and RaFD datasets, which can effectively improve the accuracy of facial expression recognition. |
| format | Article |
| id | doaj-art-074b43a478624a22a61b2a3528defd8a |
| institution | DOAJ |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2023-04-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-074b43a478624a22a61b2a3528defd8a2025-08-20T03:13:04ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832023-04-01280211712710.15938/j.jhust.2023.02.014Facial Expression Recognition with Multi-perspective Feature Fusion Under Deep Residual ConvolutionGUAN Xiaorui0GAO Lu1SONG Wenbo2LIN Kezheng3School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, ChinaHarbin Institute of Information Technology, 150431, ChinaSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China Aiming at the problems of inaccurate facial expression recognition and large amount of calculation under multi-perspective in real life, a facial expression recognition model MVResNet-FER is proposed, which is based on multi-perspective feature fusion under deep residual convolution.The residual block in ResNet is first improved and the conventional convolutional network is replaced with a depthwise separable network.Second, a CBAM module is added to enhance the extraction of effective features under multi-perspective and the supplementation of shallow feature information. Then use the RReLu activation function to replace the original ReLu to avoid deactivation of some nodes when the gradient is large.Finally, the global average pooling layer is used instead of the fully connected layer to achieve dimensionality reduction, and the generated feature vector is sent to Softmax for classification Experiments show that the proposed method produces excellent results on the CK+ and RaFD datasets, which can effectively improve the accuracy of facial expression recognition.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2203multi-perspectivefeature fusiondepth separable convolutionresidual modelexpression recognition |
| spellingShingle | GUAN Xiaorui GAO Lu SONG Wenbo LIN Kezheng Facial Expression Recognition with Multi-perspective Feature Fusion Under Deep Residual Convolution Journal of Harbin University of Science and Technology multi-perspective feature fusion depth separable convolution residual model expression recognition |
| title | Facial Expression Recognition with Multi-perspective Feature Fusion Under Deep Residual Convolution |
| title_full | Facial Expression Recognition with Multi-perspective Feature Fusion Under Deep Residual Convolution |
| title_fullStr | Facial Expression Recognition with Multi-perspective Feature Fusion Under Deep Residual Convolution |
| title_full_unstemmed | Facial Expression Recognition with Multi-perspective Feature Fusion Under Deep Residual Convolution |
| title_short | Facial Expression Recognition with Multi-perspective Feature Fusion Under Deep Residual Convolution |
| title_sort | facial expression recognition with multi perspective feature fusion under deep residual convolution |
| topic | multi-perspective feature fusion depth separable convolution residual model expression recognition |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2203 |
| work_keys_str_mv | AT guanxiaorui facialexpressionrecognitionwithmultiperspectivefeaturefusionunderdeepresidualconvolution AT gaolu facialexpressionrecognitionwithmultiperspectivefeaturefusionunderdeepresidualconvolution AT songwenbo facialexpressionrecognitionwithmultiperspectivefeaturefusionunderdeepresidualconvolution AT linkezheng facialexpressionrecognitionwithmultiperspectivefeaturefusionunderdeepresidualconvolution |