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MDEmoNet: A Multimodal Driver Emotion Recognition Network for Smart Cockpit
Hu, Chenhao1; Gu, Shenyu1; Yang, Mengjie1; Han, Gang2; Lai, Chun Sing3; Gao, Mingyu1; Yang, Zhexun4; Ma, Guojin1
2024
Source PublicationDigest of Technical Papers - IEEE International Conference on Consumer Electronics
Original Document TypeConference article (CA)
AbstractThe automotive smart cockpit is an intelligent and connected in-vehicle consumer electronics product. It can provide a safe, efficient, comfortable, and enjoyable human-machine interaction experience. Emotion recognition technology can help the smart cockpit better understand the driver's needs and state, improve the driving experience, and enhance safety. Currently, driver emotion recognition faces some challenges, such as low accuracy and high latency. In this paper, we propose a multimodal driver emotion recognition model. To our best knowledge, it is the first time to improve the accuracy of driver emotion recognition by using facial video and driving behavior (including brake pedal force, vehicle Y-Axis position and Z-Axis position) as inputs and employing a multi-Task training approach. For verification, the proposed scheme is compared with some mainstream state-of-The-Art methods on the publicly available multimodal driver emotion dataset PPB-Emo. © 2024 IEEE.
DOI10.1109/ICCE59016.2024.10444365
Language英语
ISSN0747-668X
Indexed ByEI
EI Accession Number20241115715582
PublisherInstitute of Electrical and Electronics Engineers Inc.
EISSN2159-1423
Conference Name2024 IEEE International Conference on Consumer Electronics, ICCE 2024
Conference DateJanuary 6, 2024 - January 8, 2024
Conference PlaceLas Vegas, NV, United states
Citation statistics
Cited Times [WOS]:-1   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.cug.edu.cn/handle/2XU834YA/360988
Collection中国地质大学(武汉)
Corresponding AuthorHu, Chenhao
Affiliation1.Hangzhou Dianzi University Equipment Electronics Key Lab, School of Electronics and Information, Hangzhou, China
2.Xi'An University of Posts and Telecommunications, National Engineering Laboratory for Wireless Security, Xi'an, China
3.Brunel University London, Department of Electronic and Electrical Engineering, London, United Kingdom
4.China University of Geosciences Wu Han, Faculty of Materials Science and Chemistry, Wuhan, China
Recommended Citation
GB/T 7714
Hu, Chenhao,Gu, Shenyu,Yang, Mengjie,et al. MDEmoNet: A Multimodal Driver Emotion Recognition Network for Smart Cockpit[C]:Institute of Electrical and Electronics Engineers Inc.,2024.
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