Abstract
Emotion recognition,as a step toward mind reading,seeks to infer internal states from external this http URL existing methods rely on explicit signals-such as facial expressions,speech,or gestures-that reflect only bodily responses and overlook the influence of environmental this http URL cues are often voluntary,easy to mask,and insufficient for capturing deeper,implicit emotions. Physiological signal-based approaches offer more direct access to internal states but require complex sensors that compromise natural behavior and limit this http URL-based methods typically rely on static fixation analysis and fail to capture the rich,dynamic interactions between gaze and the environment,and thus cannot uncover the deep connection between emotion and implicit this http URL address these limitations,we propose a novel camera-based,user-unaware emotion recognition approach that integrates gaze fixation patterns with environmental semantics and temporal this http URL standard HD cameras,our method unobtrusively captures users'eye appearance and head movements in natural settings-without the need for specialized hardware or active user this http URL these visual cues,the system estimates gaze trajectories over time and space, providing the basis for modeling the spatial, semantic,and temporal dimensions of gaze behavior. This allows us to capture the dynamic interplay between visual attention and the surrounding environment,revealing that emotions are not merely physiological responses but complex outcomes of human-environment this http URL proposed approach enables user-unaware,real-time,and continuous emotion recognition,offering high generalizability and low deployment cost.
Abstract (translated)
情绪识别作为一种迈向读心术的步骤，旨在从外部信号推断出内部状态。现有方法依赖于如面部表情、言语或手势等明确信号，这些信号仅反映了身体反应，并且忽略了环境线索的影响。由于这些提示往往是自愿产生的，容易被掩盖，并不足以捕捉深层的隐性情绪。 基于生理信号的方法提供了更直接访问内心状态的方式，但需要复杂的传感器，这会干扰自然行为并限制其应用范围。而基于视觉的方法通常依赖于静态注视分析，无法捕获凝视与环境之间的丰富、动态互动，因此也无法揭示情绪和隐性认知之间的深层联系。 为了解决这些局限性，我们提出了一种新颖的相机为基础的情绪识别方法，该方法不需用户知情且集成了注视模式与环境语义及时间线索。通过使用标准高清摄像机，我们的方法可以在自然环境中无须专用硬件或主动用户参与的情况下捕捉用户的外观和头部动作。 利用这些视觉提示，系统可以估计出随着时间及空间变化的凝视轨迹，从而为建模注视行为的空间、语义和时间维度提供了基础。这使我们能够捕捉到视觉注意力与周围环境之间的动态互动，揭示情绪不仅仅是一种生理反应，而是人类与环境复杂交互的结果。 所提出的这种方法实现了无意识用户参与、实时且连续的情绪识别，并具有高泛化能力和低部署成本。
URL
https://arxiv.org/abs/2507.12889
