Micro-attention for micro-expression recognition
文献类型：期刊论文
| 作者 | Wang, Chongyang1; Peng, Min2 ; Bi, Tao1; Chen, Tong3,4
|
| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2020-10-14 |
| 卷号 | 410页码:354-362 |
| 关键词 | Micro expression recognition Deep learning Attention mechanism Transfer learning |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2020.06.005 |
| 通讯作者 | Peng, Min(pengmin@cigit.ac.cn) |
| 英文摘要 | Micro-expression, for its high objectivity in emotion detection, has emerged to be a promising modality in affective computing. Recently, deep learning methods have been successfully introduced into the micro-expression recognition area. Whilst the higher recognition accuracy achieved, substantial challenges in micro-expression recognition remain. The existence of micro expression in small-local areas on face and limited size of available databases still constrain the recognition accuracy on such emotional facial behavior. In this work, to tackle such challenges, we propose a novel attention mechanism called micro-attention cooperating with residual network. Micro-attention enables the network to learn to focus on facial areas of interest covering different action units. Moreover, coping with small datasets, the micro-attention is designed without adding noticeable parameters while a simple yet efficient transfer learning approach is together utilized to alleviate the overfitting risk. With extensive experimental evaluations on three benchmarks (CASMEII, SAMM and SMIC) and post-hoc feature visualizations, we demonstrate the effectiveness of the proposed micro-attention and push the boundary of automatic recognition of micro-expression. (C) 2020 Elsevier B.V. All rights reserved. |
| 资助项目 | UCL Overseas Research Scholarship (ORS) ; UCL Graduate Research Scholarship (GRS) |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000579799300030 |
| 出版者 | ELSEVIER |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/11886]  |
| 专题 | 中国科学院重庆绿色智能技术研究院 |
| 通讯作者 | Peng, Min |
| 作者单位 | 1.UCL, UCL Interact Ctr, London, England 2.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Intelligent Secur Ctr, Chongqing, Peoples R China 3.Southwest Univ, Coll Elect & Informat Engn, Chongqing, Peoples R China 4.Chinese Acad Sci, Inst Psychol, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Chongyang,Peng, Min,Bi, Tao,et al. Micro-attention for micro-expression recognition[J]. NEUROCOMPUTING,2020,410:354-362. |
| APA | Wang, Chongyang,Peng, Min,Bi, Tao,&Chen, Tong.(2020).Micro-attention for micro-expression recognition.NEUROCOMPUTING,410,354-362. |
| MLA | Wang, Chongyang,et al."Micro-attention for micro-expression recognition".NEUROCOMPUTING 410(2020):354-362. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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