Driver emotion recognition based on attentional convolutional network
Unstable emotions, particularly anger, have been identified as significant contributors to traffic accidents. To address this issue, driver emotion recognition emerges as a promising solution within the realm of cyber-physical-social systems (CPSS). In this paper, we introduce SVGG, an emotion recog...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-04-01
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| Series: | Frontiers in Physics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2024.1387338/full |
| _version_ | 1797202045492527104 |
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| author | Xing Luan Quan Wen Bo Hang |
| author_facet | Xing Luan Quan Wen Bo Hang |
| author_sort | Xing Luan |
| collection | DOAJ |
| description | Unstable emotions, particularly anger, have been identified as significant contributors to traffic accidents. To address this issue, driver emotion recognition emerges as a promising solution within the realm of cyber-physical-social systems (CPSS). In this paper, we introduce SVGG, an emotion recognition model that leverages the attention mechanism. We validate our approach through comprehensive experiments on two distinct datasets, assessing the model’s performance using a range of evaluation metrics. The results suggest that the proposed model exhibits improved performance across both datasets. |
| first_indexed | 2024-04-24T07:57:12Z |
| format | Article |
| id | doaj.art-6cfa8d75f9b14ad4a6808758012f318e |
| institution | Directory Open Access Journal |
| issn | 2296-424X |
| language | English |
| last_indexed | 2024-04-24T07:57:12Z |
| publishDate | 2024-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj.art-6cfa8d75f9b14ad4a6808758012f318e2024-04-18T04:59:19ZengFrontiers Media S.A.Frontiers in Physics2296-424X2024-04-011210.3389/fphy.2024.13873381387338Driver emotion recognition based on attentional convolutional networkXing Luan0Quan Wen1Bo Hang2College of Communication Engineering, Jilin University, Changchun, ChinaCollege of Communication Engineering, Jilin University, Changchun, ChinaHubei University of Arts and Science, Xiangyang, ChinaUnstable emotions, particularly anger, have been identified as significant contributors to traffic accidents. To address this issue, driver emotion recognition emerges as a promising solution within the realm of cyber-physical-social systems (CPSS). In this paper, we introduce SVGG, an emotion recognition model that leverages the attention mechanism. We validate our approach through comprehensive experiments on two distinct datasets, assessing the model’s performance using a range of evaluation metrics. The results suggest that the proposed model exhibits improved performance across both datasets.https://www.frontiersin.org/articles/10.3389/fphy.2024.1387338/fullroad rage detectiondriver emotion recognitionfacial expression recognitionattention mechanismdeep learning |
| spellingShingle | Xing Luan Quan Wen Bo Hang Driver emotion recognition based on attentional convolutional network Frontiers in Physics road rage detection driver emotion recognition facial expression recognition attention mechanism deep learning |
| title | Driver emotion recognition based on attentional convolutional network |
| title_full | Driver emotion recognition based on attentional convolutional network |
| title_fullStr | Driver emotion recognition based on attentional convolutional network |
| title_full_unstemmed | Driver emotion recognition based on attentional convolutional network |
| title_short | Driver emotion recognition based on attentional convolutional network |
| title_sort | driver emotion recognition based on attentional convolutional network |
| topic | road rage detection driver emotion recognition facial expression recognition attention mechanism deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2024.1387338/full |
| work_keys_str_mv | AT xingluan driveremotionrecognitionbasedonattentionalconvolutionalnetwork AT quanwen driveremotionrecognitionbasedonattentionalconvolutionalnetwork AT bohang driveremotionrecognitionbasedonattentionalconvolutionalnetwork |