Vision-based gaze estimation: a review

Xinming Wang, Jianhua Zhang, Hanlin Zhang, Shuwen Zhao, Honghai Liu

Research output: Contribution to journalArticlepeer-review


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 inter-person 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 paper aims to review and discuss existing methods addressing above-mentioned problems, gaze involved applications and datasets 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 meta learning, causal inference, disentangled representation, and social gaze behaviour for unconstrained gaze estimation.

Original languageEnglish
JournalIEEE Transactions on Cognitive and Developmental Systems
Early online date17 Mar 2021
Publication statusEarly online - 17 Mar 2021


  • 3D Gaze Estimation
  • Computer Vision.
  • Estimation
  • Faces
  • Feature extraction
  • Head pose
  • Iris
  • Optical axis
  • Solid modeling
  • Three-dimensional displays
  • Visual axis
  • Visualization


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