CogEmoNet: A Cognitive-Feature-Augmented Driver Emotion Recognition Model for Smart Cockpit

doi:10.1109/TCSS.2021.3127935

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

	CogEmoNet: A Cognitive-Feature-Augmented Driver Emotion Recognition Model for Smart Cockpit
	Li, Wenbo 7; Zeng, Guanzhong 7; Zhang, Juncheng 7; Xu, Yan 6; Xing, Yang 1; Zhou, Rui 5; Guo, Gang 7; Shen, Yu 4; Cao, Dongpu 3; Wang, Fei-Yue 2
刊名	IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
	2021-11-29
页码	12
关键词	Emotion recognition Feature extraction Cognitive processes Face recognition Task analysis Information processing Convolutional neural networks Affective computing driver emotion facial expression human-machine interaction (HMI) smart cockpit
ISSN号	2329-924X
DOI	10.1109/TCSS.2021.3127935
通讯作者	Guo, Gang(guogang@cqu.edu.cn)
英文摘要	Driver's emotion recognition is vital to improving driving safety, comfort, and acceptance of intelligent vehicles. This article presents a cognitive-feature-augmented driver emotion detection method that is based on emotional cognitive process theory and deep networks. Different from the traditional methods, both the driver's facial expression and cognitive process characteristics (age, gender, and driving age) were used as the inputs of the proposed model. Convolutional techniques were adopted to construct the model for driver's emotion detection simultaneously considering the driver's facial expression and cognitive process characteristics. A driver's emotion data collection was carried out to validate the performance of the proposed method. The collected dataset consists of 40 drivers' frontal facial videos, their cognitive process characteristics, and self-reported assessments of driver emotions. Another two deep networks were also used to compare recognition performance. The results prove that the proposed method can achieve well detection results for different databases on the discrete emotion model and dimensional emotion model, respectively.
WOS关键词	IDENTIFICATION ; NETWORK
WOS研究方向	Computer Science
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000727916600001
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/46770]
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	Guo, Gang
作者单位	1.Cranfield Univ, Dept Aerosp Transport & Mfg, Cranfield MK43 0AL, Beds, England 2.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China 3.Univ Waterloo, Dept Mech & Mechatron Engn, Waterloo, ON N2L 3G1, Canada 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 5.Waytous Inc, Dept Res & Dev, Shenzhen, Peoples R China 6.Univ Sci & Technol Beijing, Dept Mech Engn, Beijing 100083, Peoples R China 7.Chongqing Univ, Coll Mech & Vehicle Engn, Chongqing 400044, Peoples R China
推荐引用方式 GB/T 7714	Li, Wenbo,Zeng, Guanzhong,Zhang, Juncheng,et al. CogEmoNet: A Cognitive-Feature-Augmented Driver Emotion Recognition Model for Smart Cockpit[J]. IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,2021:12.
APA	Li, Wenbo.,Zeng, Guanzhong.,Zhang, Juncheng.,Xu, Yan.,Xing, Yang.,...&Wang, Fei-Yue.(2021).CogEmoNet: A Cognitive-Feature-Augmented Driver Emotion Recognition Model for Smart Cockpit.IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,12.
MLA	Li, Wenbo,et al."CogEmoNet: A Cognitive-Feature-Augmented Driver Emotion Recognition Model for Smart Cockpit".IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS (2021):12.