HybridGazeNet: Geometric model guided Convolutional Neural Networks for gaze estimation

• URL: http://arxiv.org/abs/2111.11691v1
• Date: Tue, 23 Nov 2021 07:20:37 GMT
• Title: HybridGazeNet: Geometric model guided Convolutional Neural Networks for gaze estimation
• Authors: Shaobo Guo, Xiao Jiang, Zhizhong Su, Rui Wu and Xin Wang
• Abstract summary: We propose HybridGazeNet, a unified framework that encodes the geometric eyeball model into the appearance-based CNN architecture explicitly. Experiments on multiple challenging gaze datasets shows that HybridGazeNet has better accuracy and generalization ability compared with existing SOTA methods.
• Score: 9.649076368863904
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As a critical cue for understanding human intention, human gaze provides a key signal for Human-Computer Interaction(HCI) applications. Appearance-based gaze estimation, which directly regresses the gaze vector from eye images, has made great progress recently based on Convolutional Neural Networks(ConvNets) architecture and open-source large-scale gaze datasets. However, encoding model-based knowledge into CNN model to further improve the gaze estimation performance remains a topic that needs to be explored. In this paper, we propose HybridGazeNet(HGN), a unified framework that encodes the geometric eyeball model into the appearance-based CNN architecture explicitly. Composed of a multi-branch network and an uncertainty module, HybridGazeNet is trained using a hyridized strategy. Experiments on multiple challenging gaze datasets shows that HybridGazeNet has better accuracy and generalization ability compared with existing SOTA methods. The code will be released later.

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