2020/04/26 by Wenbo Li, Yaodong Cui, Li, Wenbo +11 · 1 citation
Neuroscience · Psychology · #Computer Vision and Pattern Recognition (cs.CV) #EEG and Brain-Computer Interfaces #Emotion and Mood Recognition #FOS: Computer and information sciences #Sleep and Work-Related Fatigue
paper · pdf · doi:10.48550/arxiv.2005.08626
openalex publication_date 2020/04/26 · openalex created_date 2020/05/21 · openalex updated_date 2026/07/28
In this paper, we introduce a new dataset, the driver emotion facial expression (DEFE) dataset, for driver spontaneous emotions analysis. The dataset includes facial expression recordings from 60 participants during driving. After watching a selected video-audio clip to elicit a specific emotion, each participant completed the driving tasks in the same driving scenario and rated their emotional responses during the driving processes from the aspects of dimensional emotion and discrete emotion. We also conducted classification experiments to recognize the scales of arousal, valence, dominance, as well as the emotion category and intensity to establish baseline results for the proposed dataset. Besides, this paper compared and discussed the differences in facial expressions between driving and non-driving scenarios. The results show that there were significant differences in AUs (Action Units) presence of facial expressions between driving and non-driving scenarios, indicating that human emotional expressions in driving scenarios were different from other life scenarios. Therefore, publishing a human emotion dataset specifically for the driver is necessary for traffic safety improvement. The proposed dataset will be publicly available so that researchers worldwide can use it to develop and examine their driver emotion analysis methods. To the best of our knowledge, this is currently the only public driver facial expression dataset.