Gazing point dependent eye gaze estimation

Abstract

Cross-ratio invariant is used widely in projective transformations for eye gaze estimation. Establishing a virtual plane projection is an important step to use this property. Most of traditional cross-ratio approaches only used fixed parameters to calculate the gazing point. This paper proposes gazing point dependent eye gazing estimation approach. Our contributions are three-folded. First, we model a dynamic virtual plane projection, which is tangent to the cornea of pupil, to estimate the position of the gazing point. Second, we introduce a two stage approach consisting of rough-to-precise framework for gazing point estimation based on the gazing point dependent virtual plane projection. Third, a heuristic strategy which contains off-line and on-line parameter learning for gazing point estimation is proposed. The experiment results show that our approach can significantly improve the gazing estimation performance with an average accuracy of 0.70°.