Res-RBG Facial Expression Recognition in Image Sequences Based on Dual Neural Networks
Facial expressions involve dynamic changes, and facial expression recognition based on static images struggles to capture the temporal information inherent in these dynamic changes. The resultant degradation in real-world performance critically impedes the integration of facial expression recognitio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/12/3829 |
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| author | Xiangwei Mou Yongfu Song Xiuping Xie Mingxuan You Rijun Wang |
| author_facet | Xiangwei Mou Yongfu Song Xiuping Xie Mingxuan You Rijun Wang |
| author_sort | Xiangwei Mou |
| collection | DOAJ |
| description | Facial expressions involve dynamic changes, and facial expression recognition based on static images struggles to capture the temporal information inherent in these dynamic changes. The resultant degradation in real-world performance critically impedes the integration of facial expression recognition systems into intelligent sensing applications. Therefore, this paper proposes a facial expression recognition method for image sequences based on the fusion of dual neural networks (ResNet and residual bidirectional GRU—Res-RBG). The model proposed in this paper achieves recognition accuracies of 98.10% and 88.64% on the CK+ and Oulu-CASIA datasets, respectively. Moreover, the model has a parameter size of only 64.20 M. Compared to existing methods for image sequence-based facial expression recognition, the approach presented in this paper demonstrates certain advantages, indicating strong potential for future edge sensor deployment. |
| format | Article |
| id | doaj-art-07a6610f5ff3461dadd141ebde79bd2b |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-07a6610f5ff3461dadd141ebde79bd2b2025-08-20T03:26:51ZengMDPI AGSensors1424-82202025-06-012512382910.3390/s25123829Res-RBG Facial Expression Recognition in Image Sequences Based on Dual Neural NetworksXiangwei Mou0Yongfu Song1Xiuping Xie2Mingxuan You3Rijun Wang4College of Electronic and Information Engineering/Integrated Circuits, Guangxi Normal University, Guilin 541004, ChinaCollege of Electronic and Information Engineering/Integrated Circuits, Guangxi Normal University, Guilin 541004, ChinaCollege of Electronic and Information Engineering/Integrated Circuits, Guangxi Normal University, Guilin 541004, ChinaCollege of Electronic and Information Engineering/Integrated Circuits, Guangxi Normal University, Guilin 541004, ChinaTeachers College for Vocational and Technical Education, Guangxi Normal University, Guilin 541004, ChinaFacial expressions involve dynamic changes, and facial expression recognition based on static images struggles to capture the temporal information inherent in these dynamic changes. The resultant degradation in real-world performance critically impedes the integration of facial expression recognition systems into intelligent sensing applications. Therefore, this paper proposes a facial expression recognition method for image sequences based on the fusion of dual neural networks (ResNet and residual bidirectional GRU—Res-RBG). The model proposed in this paper achieves recognition accuracies of 98.10% and 88.64% on the CK+ and Oulu-CASIA datasets, respectively. Moreover, the model has a parameter size of only 64.20 M. Compared to existing methods for image sequence-based facial expression recognition, the approach presented in this paper demonstrates certain advantages, indicating strong potential for future edge sensor deployment.https://www.mdpi.com/1424-8220/25/12/3829facial expression recognitionfeature extractiondeep learningBi-GRUresidual structureattention mechanism |
| spellingShingle | Xiangwei Mou Yongfu Song Xiuping Xie Mingxuan You Rijun Wang Res-RBG Facial Expression Recognition in Image Sequences Based on Dual Neural Networks Sensors facial expression recognition feature extraction deep learning Bi-GRU residual structure attention mechanism |
| title | Res-RBG Facial Expression Recognition in Image Sequences Based on Dual Neural Networks |
| title_full | Res-RBG Facial Expression Recognition in Image Sequences Based on Dual Neural Networks |
| title_fullStr | Res-RBG Facial Expression Recognition in Image Sequences Based on Dual Neural Networks |
| title_full_unstemmed | Res-RBG Facial Expression Recognition in Image Sequences Based on Dual Neural Networks |
| title_short | Res-RBG Facial Expression Recognition in Image Sequences Based on Dual Neural Networks |
| title_sort | res rbg facial expression recognition in image sequences based on dual neural networks |
| topic | facial expression recognition feature extraction deep learning Bi-GRU residual structure attention mechanism |
| url | https://www.mdpi.com/1424-8220/25/12/3829 |
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