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Learning a 3D gaze estimator with adaptive weighted strategy

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journal contribution
posted on 19.03.2021, 09:07 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.

Funding

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

History

School

  • Science

Department

  • Computer Science

Published in

IEEE Access

Volume

8

Pages

82142 - 82152

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Version

VoR (Version of Record)

Publisher statement

This is an Open Access Article. It is published by IEEE under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

Acceptance date

23/04/2020

Publication date

2020-04-27

Copyright date

2020

eISSN

2169-3536

Language

en

Depositor

Dr Haibin Cai. Deposit date: 17 March 2021