Relation-Aware Facial Expression Recognition

doi:10.1109/TCDS.2021.3100131

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进控制与自动化团队

	Relation-Aware Facial Expression Recognition
	Xia, Yifan 5; Yu, Hui 5; Wang, Xiao 2; Jian, Muwei 3,5; Wang, Fei-Yue 1,4,6
刊名	IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
	2022-09-01
卷号	14 期号:3 页码:1143-1154
关键词	Face recognition Feature extraction Mouth Image recognition Deep learning Databases Convolutional neural networks Deep convolutional neural networks facial expression in the wild facial expression recognition relation convolutional neural network (ReCNN) relation-aware
ISSN号	2379-8920
DOI	10.1109/TCDS.2021.3100131
通讯作者	Yu, Hui(hui.yu@port.ac.uk) ; Wang, Fei-Yue(feiyue.wang@ia.ac.cn)
英文摘要	Research on facial expression recognition has been moving from the constrained lab scenarios to the in-the-wild situations and has made progress in recent years. However, it is still very challenging to deal with facial expression in the wild due to large poses and occlusion as well as illumination and intensity variations. Generally, existing methods mainly take the whole face as a uniform source of features for facial expression analysis. Actually, physiology and psychology research shows that some crucial regions, such as the eye and mouth, reflect the differences of different facial expressions, which have close relationships with emotion expression. Inspired by this observation, a novel relation-aware facial expression recognition method called relation convolutional neural network (ReCNN) is proposed in this article, which can adaptively capture the relationship between crucial regions and facial expressions leading to the focus on the most discriminative regions for recognition. We have evaluated the proposed ReCNN on two large in-the-wild databases: 1) AffectNet and 2) RAF-DB. Extensive experiments on these databases show that our method has superior recognition accuracy compared with state-of-the-art methods and the relationship between crucial regions and facial expressions is beneficial to improve the performance of facial expression recognition.
资助项目	Engineering and Physical Sciences Research Council (EPSRC)[EP/N025849/1] ; Royal Academy of Engineering[IFS1819/9]
WOS关键词	NETWORK ; FACE
WOS研究方向	Computer Science ; Robotics ; Neurosciences & Neurology
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000852243600034
资助机构	Engineering and Physical Sciences Research Council (EPSRC) ; Royal Academy of Engineering
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/50098]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者	Yu, Hui; Wang, Fei-Yue
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Qingdao Acad Intelligent Ind, Parallel Intelligence Innovat Ctr, Qingdao 266109, Peoples R China 3.Shandong Univ Finance & Econ, Sch Comp Sci & Technol, Jinan 250014, Peoples R China 4.Qingdao Acad Intelligent Ind, Qingdao 266109, Peoples R China 5.Univ Portsmouth, Sch Creat Technol, Portsmouth PO1 2DJ, Hants, England 6.Macau Univ Sci & Technol, Inst Syst Engn, Taipa, Macao, Peoples R China
推荐引用方式 GB/T 7714	Xia, Yifan,Yu, Hui,Wang, Xiao,et al. Relation-Aware Facial Expression Recognition[J]. IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS,2022,14(3):1143-1154.
APA	Xia, Yifan,Yu, Hui,Wang, Xiao,Jian, Muwei,&Wang, Fei-Yue.(2022).Relation-Aware Facial Expression Recognition.IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS,14(3),1143-1154.
MLA	Xia, Yifan,et al."Relation-Aware Facial Expression Recognition".IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 14.3(2022):1143-1154.