PSYCH OpenIR
Micro-attention for micro-expression recognition
Wang, Chongyang1; Peng, Min2; Bi, Tao1; Chen, Tong3,4
First AuthorWang, Chongyang
Corresponding AuthorPeng, Min(pengmin@cigit.ac.cn)
Correspondent Emailpengmin@cigit.ac.cn (m. peng)
Abstract

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.

KeywordMicro expression recognition Deep learning Attention mechanism Transfer learning
2020
Language英语
DOI10.1016/j.neucom.2020.06.005
Source PublicationNeurocomputing
ISSN0925-2312
Volume410Pages:354-362
Subtypearticle
Indexed ByEI
Funding ProjectUCL Overseas Research Scholarship (ORS) ; UCL Graduate Research Scholarship (GRS)
PublisherELSEVIER
WOS KeywordCATEGORIZATION
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000579799300030
Funding OrganizationUCL Overseas Research Scholarship (ORS) ; UCL Graduate Research Scholarship (GRS)
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.psych.ac.cn/handle/311026/32218
Collection中国科学院心理研究所
Affiliation1.UCL Interaction Centre, University College London, London, United Kingdom
2.College of Electronic and Information Engineering, Southwest University, Chongqing, China
3.College of Electronic and Information Engineering, Southwest University, Chongqing, China
4.Intelligent Security Center, Chongqing Institute of Green and Intelligent Technology, CAS, Chongqing, China
Recommended Citation
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.
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