Emotion Prediction Through Eye Tracking in Affective Computing Systems

Affective systems are a class of pervasive systems that aim to support humans at all levels, including critical interactions with medical services or learning platforms by measuring, understanding, and acting on behalf of affect, feelings, and emotions. In order to react appropriately, affective systems need to correctly read their human counterpart. In this paper, eye tracking is proposed as a non-invasive tool to measure the affective quality of videos. We collect gaze data from 175 subjects watching eight videos and assess how fixation features react to the affective content. While several features are biased by video-specific effects of dynamics and sound and show no sensitivity to affect after correcting for these effects, the average fixation duration and dwell time preserve discriminating patterns. On videos triggering negative affect, the gaze is focused, with meticulous examinations of the risk inducing element – characterized by longer dwells and shorter fixation duration. Positively connoted videos lead to comprehensive examinations of the entire scene, marked by shorter dwells and fixation duration.