Model-based deep gaze estimation using incrementally updated face-shape parameters
Makoto Sei, Akira Utsumi, Hirotake Yamazoe, Joo‐Ho Lee · 2023
In this paper, we propose a method to improve the performance of deep gaze estimation using face-shape parameters adapted to a specific target person based on multiple observations. Our gaze estimation network contains a predefined computation module that calculates gaze directions using known geometric relationships among head poses, eye-ball positions, and gaze directions. Updated face-shape parameters contribute to improving the performance of the process. In addition, the computation module enables a network to acquire the ability to induce hidden parameters such as eyeball position and eyeball radius from observed information through a training process. Experimental results reveal improvement in gaze estimation accuracy by introducing a sequential update process for face-shape parameters and a predefined computation module.