Learning a 3D gaze estimator with adaptive weighted strategy
journal contributionposted on 2021-03-19, 09:07 authored by Xiaolong Zhou, Jiaqi Jiang, Qianqian Liu, Jianwen Fang, Shengyong Chen, Haibin CaiHaibin Cai
As a method of predicting the target’s attention distribution, gaze estimation plays an important role in human-computer interaction. In this paper, we learn a 3D gaze estimator with adaptive weighted strategy to get the mapping from the complete images to the gaze vector. We select the both eyes, the complete face and their fusion features as the input of the regression model of gaze estimator. Considering that the different areas of the face have different contributions on the results of gaze estimation under free head movement, we design a new learning strategy for the regression net. To improve the efficiency of the regression model to a great extent, we propose a weighted network that can adjust the learning strategy of the regression net adaptively. Experimental results conducted on the MPIIGaze and EyeDiap datasets demonstrate that our method can achieve superior performance compared with other state-of-the-art 3D gaze estimation methods.
National Natural Science Foundation of China under Grant U1709207, Grant 61876168, and Grant 61906168
National Key Research and Development Program of China under Grant 2018YFB1305200
Zhejiang Provincial Natural Science Foundation of China under Grant LY18F030020 and Grant LY15F020041
- Computer Science