Real-time gaze tracking provides crucial input to psychophysics studies and neuromarketing applications. Many of the modern eye-tracking solutions are expensive mainly due to the high-end processing hardware specialized for processing infrared-camera pictures. Here, we introduce a deep learning-based approach which uses the frames of low-cost web cameras. Using DeepLabCut, an open-source toolbox for extracting points of interest from videos, we obtained facial landmarks critical to gaze location and estimated the point of gaze on a computer screen via a shallow neural network. Tested for three extreme poses, this architecture reached a median error of about one degree of visual angle. Our results contribute to the growing field of deep-learning approaches to eye-tracking, laying the foundation for further investigation by researchers in psychophysics or neuromarketing.

中文翻译：

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基于深度学习的人类心理物理学视频眼动追踪方法

实时凝视跟踪为心理物理学研究和神经营销应用提供了重要的输入。许多现代眼动追踪解决方案价格昂贵，这主要是由于专门用于处理红外相机图片的高端处理硬件。在这里，我们介绍了一种基于深度学习的方法，该方法使用低成本网络摄像头的帧。使用 DeepLabCut，一个用于从视频中提取兴趣点的开源工具箱，我们获得了对凝视位置至关重要的面部标志，并通过浅层神经网络估计计算机屏幕上的凝视点。经过三个极端姿势的测试，该架构达到了大约 1 度视角的中值误差。我们的结果有助于眼动追踪深度学习方法的不断发展，