Camera-based implicit mind reading by capturing higher-order semantic dynamics of human gaze within environmental context

Emotion recognition,as a step toward mind reading,seeks to infer internal states from externalthis http URLexisting methods rely on explicit signals-such as facial expressions,speech,or gestures-that reflect only bodily responses and overlook the influence of environmentalthis http URLcues 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 limitthis 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 implicitthis http URLaddress these limitations,we propose a novel camera-based,user-unaware emotion recognition approach that integrates gaze fixation patterns with environmental semantics and temporalthis http URLstandard HD cameras,our method unobtrusively captures userséye appearance and head movements in natural settings-without the need for specialized hardware or active userthis http URLthese 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-environmentthis http URLproposed approach enables user-unaware,real-time,and continuous emotion recognition,offering high generalizability and low deployment cost.