A Spontaneous Driver Emotion Facial Expression (DEFE) Dataset for Intelligent Vehicles
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Long-Short Distance Graph Neural Networks and Improved Curriculum Learning for Emotion Recognition in Conversation
Dynamic Parameter Memory: Temporary LoRA-Enhanced LLM for Long-Sequence Emotion Recognition in Conversation
Exploring Remote Physiological Signal Measurement under Dynamic Lighting Conditions at Night: Dataset, Experiment, and Analysis
Infant Cry Emotion Recognition Using Improved ECAPA-TDNN with Multiscale Feature Fusion and Attention Enhancement
Towards Robust Multimodal Emotion Recognition under Missing Modalities and Distribution Shifts
MEDUSA: A Multimodal Deep Fusion Multi-Stage Training Framework for Speech Emotion Recognition in Naturalistic Conditions
토론
이 논문과 연결된 포럼 글이 아직 없습니다.