Micro-attention for micro-expression recognition

doi:10.1016/j.neucom.2020.06.005

	Micro-attention for micro-expression recognition
	Wang, Chongyang 1; Peng, Min 4; Bi, Tao 1; Chen, Tong 2,3
刊名	NEUROCOMPUTING
	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
出版者	ELSEVIER
WOS记录号	WOS:000579799300030
内容类型	期刊论文
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, Inst Psychol, Beijing, Peoples R China 3.Southwest Univ, Coll Elect & Informat Engn, Chongqing, Peoples R China 4.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Intelligent Secur Ctr, Chongqing, 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.