Learning to Detect Head Movement in Unconstrained Remote Gaze Estimation in the Wild

• URL: http://arxiv.org/abs/2004.03737v1
• Date: Tue, 7 Apr 2020 22:38:49 GMT
• Title: Learning to Detect Head Movement in Unconstrained Remote Gaze Estimation in the Wild
• Authors: Zhecan Wang, Jian Zhao, Cheng Lu, Han Huang, Fan Yang, Lianji Li, Yandong Guo
• Abstract summary: We propose end-to-end appearance-based gaze estimation methods that could more robustly incorporate different levels of head-pose representations into gaze estimation. Our method could generalize to real-world scenarios with low image quality, different lightings and scenarios where direct head-pose information is not available.
• Score: 19.829721663742124
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unconstrained remote gaze estimation remains challenging mostly due to its vulnerability to the large variability in head-pose. Prior solutions struggle to maintain reliable accuracy in unconstrained remote gaze tracking. Among them, appearance-based solutions demonstrate tremendous potential in improving gaze accuracy. However, existing works still suffer from head movement and are not robust enough to handle real-world scenarios. Especially most of them study gaze estimation under controlled scenarios where the collected datasets often cover limited ranges of both head-pose and gaze which introduces further bias. In this paper, we propose novel end-to-end appearance-based gaze estimation methods that could more robustly incorporate different levels of head-pose representations into gaze estimation. Our method could generalize to real-world scenarios with low image quality, different lightings and scenarios where direct head-pose information is not available. To better demonstrate the advantage of our methods, we further propose a new benchmark dataset with the most rich distribution of head-gaze combination reflecting real-world scenarios. Extensive evaluations on several public datasets and our own dataset demonstrate that our method consistently outperforms the state-of-the-art by a significant margin.

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