Generalized zero-shot emotion recognition from body gestures
文献类型：期刊论文
| 作者 | Wu, Jinting1,2 ; Zhang, Yujia1; Sun, Shiying1; Li, Qianzhong1,2; Zhao, Xiaoguang1
|
| 刊名 | APPLIED INTELLIGENCE
 |
| 出版日期 | 2021-11-01 |
| 页码 | 19 |
| 关键词 | Generalized zero-shot learning Emotion recognition Body gesture recognition Prototype learning |
| ISSN号 | 0924-669X |
| DOI | 10.1007/s10489-021-02927-w |
| 通讯作者 | Zhang, Yujia(zhangyujia2014@ia.ac.cn) |
| 英文摘要 | In human-human interaction, body language is one of the most important emotional expressions. However, each emotion category contains abundant emotional body gestures, and basic emotions used in most researches are difficult to describe complex and diverse emotional states. It is costly to collect sufficient samples of all emotional expressions, and new emotions or new body gestures that are not included in the training set may appear during testing. To address the above problems, we design a novel mechanism that treats each emotion category as a collection of multiple body gesture categories to make better use of gesture information for emotion recognition. A Generalized Zero-Shot Learning (GZSL) framework is introduced to recognize both seen and unseen body gesture categories with the help of semantic information, and emotion predictions are further provided based on the relationship between gestures and emotions. This framework consists of two branches. The first branch is a Hierarchical Prototype Network (HPN) which learns the prototypes of body gestures and uses them to calculate the emotion attentive prototypes. This branch aims to obtain predictions on samples of the seen gesture categories. The second branch is a Semantic Auto-Encoder (SAE) which utilizes semantic representations to predict samples of unseen gesture categories. Thresholds are further trained to determine which branch result will be used during testing, and the emotion labels are finally obtained from these results. Comprehensive experiments are conducted on an emotion recognition dataset which contains skeleton data of multiple body gestures, and the performance of our framework is superior to both the traditional emotion classifier and state-of-the-art zero-shot learning methods. |
| WOS关键词 | CLASSIFICATION ; MOVEMENT ; NETWORK |
| 资助项目 | National Key Research and Development Project of China[2019YFB1310601] ; National Key R&D Program of China[2017YFC0820203] ; National Natural Science Foundation of China[62103410] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000713535200001 |
| 出版者 | SPRINGER |
| 资助机构 | National Key Research and Development Project of China ; National Key R&D Program of China ; National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/46353]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队 |
| 通讯作者 | Zhang, Yujia |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wu, Jinting,Zhang, Yujia,Sun, Shiying,et al. Generalized zero-shot emotion recognition from body gestures[J]. APPLIED INTELLIGENCE,2021:19. |
| APA | Wu, Jinting,Zhang, Yujia,Sun, Shiying,Li, Qianzhong,&Zhao, Xiaoguang.(2021).Generalized zero-shot emotion recognition from body gestures.APPLIED INTELLIGENCE,19. |
| MLA | Wu, Jinting,et al."Generalized zero-shot emotion recognition from body gestures".APPLIED INTELLIGENCE (2021):19. |
入库方式： OAI收割
来源：自动化研究所
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