Abstract
Eye gaze is an important natural behavior in social interaction as it delivers complex exchanges between observer and observed, by building up the geometric constraints and relation of the exchanges. These interperson exchanges can be modeled based on gaze direction estimated using computer vision. Despite significant progresses in vision-based gaze estimation in last 10 years, it is still nontrivial since the accuracy of gaze estimation is significantly affected by such intrinsic factors as head pose variance, individual bias between optical axis and visual axis, eye blink, occlusion and image blur, degrade gaze features, lead to inaccurate gaze-involved human social interaction analysis. This article aims to review and discuss existing methods addressing above-mentioned problems, gaze involved applications and data sets against the state of the arts in vision-based gaze estimation. It also points out future research directions and challenges of gaze estimation in terms of metalearning, causal inference, disentangled representation, and social gaze behavior for unconstrained gaze estimation.
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