面向面对面场景的情感检测：眼动时间动态与人格的交互建模
Modelling the Interplay of Eye-Tracking Temporal Dynamics and Personality for Emotion Detection in Face-to-Face Settings
AI总结：
针对动态场景情绪识别难题，提出融合眼动时序动态、大五人格与刺激线索的人格感知多模态框架，在CREMA-D数据集上验证了不同信息对感知/感受情绪的差异化提升作用，推动个性化情感计算发展。
摘要：
准确识别人类情绪对自适应人机交互至关重要，但在动态对话式场景中仍具挑战性。本文提出一种具备人格感知的多模态框架，整合眼动序列、大五人格特质与情境刺激线索，以预测感知情绪与感受情绪。73名受试者观看了来自CREMA-D数据集的含语音片段，同时提供眼动信号、人格评估结果与情绪评分。我们的神经模型捕捉了时间注视动态，并将其与特质及刺激信息融合，性能较SVM与现有文献基线实现了稳定提升。结果表明：(i) 刺激线索可显著增强感知情绪预测（宏F1最高达0.77）；(ii) 人格特质对感受情绪识别的提升幅度最大（宏F1最高达0.58）。这些发现凸显了结合生理、特质层面与情境信息以应对情绪固有主观性的优势。通过区分感知与感受反应，本文方法推动了多模态情感计算发展，为更具个性化与生态效度的情绪感知系统指明方向。
英文摘要：
Accurate recognition of human emotions is critical for adaptive human-computer interaction, yet remains challenging in dynamic, conversation-like settings. This work presents a personality-aware multimodal framework that integrates eye-tracking sequences, Big Five personality traits, and contextual stimulus cues to predict both perceived and felt emotions. Seventy-three participants viewed speech-containing clips from the CREMA-D dataset while providing eye-tracking signals, personality assessments, and emotion ratings. Our neural models captured temporal gaze dynamics and fused them with trait and stimulus information, yielding consistent gains over SVM and literature baselines. Results show that (i) stimulus cues strongly enhance perceived-emotion predictions (macro F1 up to 0.77), while (ii) personality traits provide the largest improvements for felt emotion recognition (macro F1 up to 0.58). These findings highlight the benefit of combining physiological, trait-level, and contextual information to address the inherent subjectivity of emotion. By distinguishing between perceived and felt responses, our approach advances multimodal affective computing and points toward more personalized and ecologically valid emotion-aware systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
