Context-Aware Attention Network for Human Emotion Recognition in Video
Recognition of human emotion from facial expression is affected by distortions of pictorial quality and facial pose, which is often ignored by traditional video emotion recognition methods. On the other hand, context information can also provide different degrees of extra clues, which can further im...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2020/8843413 |
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| author | Xiaodong Liu Miao Wang |
| author_facet | Xiaodong Liu Miao Wang |
| author_sort | Xiaodong Liu |
| collection | DOAJ |
| description | Recognition of human emotion from facial expression is affected by distortions of pictorial quality and facial pose, which is often ignored by traditional video emotion recognition methods. On the other hand, context information can also provide different degrees of extra clues, which can further improve the recognition accuracy. In this paper, we first build a video dataset with seven categories of human emotion, named human emotion in the video (HEIV). With the HEIV dataset, we trained a context-aware attention network (CAAN) to recognize human emotion. The network consists of two subnetworks to process both face and context information. Features from facial expression and context clues are fused to represent the emotion of video frames, which will be then passed through an attention network and generate emotion scores. Then, the emotion features of all frames will be aggregated according to their emotional score. Experimental results show that our proposed method is effective on HEIV dataset. |
| format | Article |
| id | doaj-art-10d09fea222a42808224d436d5199d54 |
| institution | Kabale University |
| issn | 1687-5680 1687-5699 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-10d09fea222a42808224d436d5199d542025-02-03T01:00:18ZengWileyAdvances in Multimedia1687-56801687-56992020-01-01202010.1155/2020/88434138843413Context-Aware Attention Network for Human Emotion Recognition in VideoXiaodong Liu0Miao Wang1School of Computing Henan University of Engineering, Zhengzhou, ChinaSchool of Computing Henan University of Engineering, Zhengzhou, ChinaRecognition of human emotion from facial expression is affected by distortions of pictorial quality and facial pose, which is often ignored by traditional video emotion recognition methods. On the other hand, context information can also provide different degrees of extra clues, which can further improve the recognition accuracy. In this paper, we first build a video dataset with seven categories of human emotion, named human emotion in the video (HEIV). With the HEIV dataset, we trained a context-aware attention network (CAAN) to recognize human emotion. The network consists of two subnetworks to process both face and context information. Features from facial expression and context clues are fused to represent the emotion of video frames, which will be then passed through an attention network and generate emotion scores. Then, the emotion features of all frames will be aggregated according to their emotional score. Experimental results show that our proposed method is effective on HEIV dataset.http://dx.doi.org/10.1155/2020/8843413 |
| spellingShingle | Xiaodong Liu Miao Wang Context-Aware Attention Network for Human Emotion Recognition in Video Advances in Multimedia |
| title | Context-Aware Attention Network for Human Emotion Recognition in Video |
| title_full | Context-Aware Attention Network for Human Emotion Recognition in Video |
| title_fullStr | Context-Aware Attention Network for Human Emotion Recognition in Video |
| title_full_unstemmed | Context-Aware Attention Network for Human Emotion Recognition in Video |
| title_short | Context-Aware Attention Network for Human Emotion Recognition in Video |
| title_sort | context aware attention network for human emotion recognition in video |
| url | http://dx.doi.org/10.1155/2020/8843413 |
| work_keys_str_mv | AT xiaodongliu contextawareattentionnetworkforhumanemotionrecognitioninvideo AT miaowang contextawareattentionnetworkforhumanemotionrecognitioninvideo |