egoEMOTION：眼动视觉与生理信号用于现实任务中情绪与个性识别
egoEMOTION: Egocentric Vision and Physiological Signals for Emotion and Personality Recognition in Real-World Tasks
AI总结：
egoEMOTION通过结合眼动视觉和生理信号，首次实现现实任务中情绪与个性识别的基准任务与数据集。
摘要：
理解情绪是预测人类行为的关键，但当前眼动视觉基准大多忽略了影响决策和行动的人的情绪状态。现有的眼动感知任务集中在身体活动、手-物体交互和注意力建模上，假设中性情绪和统一性格。这限制了视觉系统捕捉行为关键内部驱动因素的能力。在本文中，我们提出了egoEMOTION，第一个结合眼动视觉和生理信号，并在受控和现实场景中密集报告情绪和性格的数据库。我们的数据库包含来自43名参与者超过50小时的记录，使用Meta的Project Aria眼镜录制。每个会话提供同步的眼动追踪视频、头戴式光脉冲波形图、惯性运动数据和生理基线作为参考。参与者在完成情绪诱发任务和自然活动的同时，使用Circumplex模型和Mikels的轮子报告自己的情绪状态，并使用Big Five模型报告自己的性格。我们定义了三个基准任务：（1）连续情绪分类（愉悦度、唤醒度、主导性）；（2）离散情绪分类；（3）特质层面的性格推断。我们表明，一种经典的学习方法，作为现实情绪预测的简单基线，在处理眼动视觉系统捕获的信号时，比处理生理信号产生更好的估计。我们的数据库确立了情绪和性格在眼动感知中的核心维度，并为基于情绪驱动的行为、意图和交互建模开辟了新方向。
英文摘要：
Understanding affect is central to anticipating human behavior, yet current egocentric vision benchmarks largely ignore the person's emotional states that shape their decisions and actions. Existing tasks in egocentric perception focus on physical activities, hand-object interactions, and attention modeling - assuming neutral affect and uniform personality. This limits the ability of vision systems to capture key internal drivers of behavior. In this paper, we present egoEMOTION, the first dataset that couples egocentric visual and physiological signals with dense self-reports of emotion and personality across controlled and real-world scenarios. Our dataset includes over 50 hours of recordings from 43 participants, captured using Meta's Project Aria glasses. Each session provides synchronized eye-tracking video, headmounted photoplethysmography, inertial motion data, and physiological baselines for reference. Participants completed emotion-elicitation tasks and naturalistic activities while self-reporting their affective state using the Circumplex Model and Mikels' Wheel as well as their personality via the Big Five model. We define three benchmark tasks: (1) continuous affect classification (valence, arousal, dominance); (2) discrete emotion classification; and (3) trait-level personality inference. We show that a classical learning-based method, as a simple baseline in real-world affect prediction, produces better estimates from signals captured on egocentric vision systems than processing physiological signals. Our dataset establishes emotion and personality as core dimensions in egocentric perception and opens new directions in affect-driven modeling of behavior, intent, and interaction.
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。
