We present a deep learning method for accurately localizing the center of a single corneal reflection (CR) in an eye image. Unlike previous approaches, we use a convolutional neural network (CNN) that was trained solely using simulated data. Using only simulated data has the benefit of completely sidestepping the time-consuming process of manual annotation that is required for supervised training on real eye images. To systematically evaluate the accuracy of our method, we first tested it on images with simulated CRs placed on different backgrounds and embedded in varying levels of noise. Second, we tested the method on high-quality videos captured from real eyes. Our method outperformed state-of-the-art algorithmic methods on real eye images with a 35% reduction in terms of spatial precision, and performed on par with state-of-the-art on simulated images in terms of spatial accuracy.We conclude that our method provides a precise method for CR center localization and provides a solution to the data availability problem which is one of the important common roadblocks in the development of deep learning models for gaze estimation. Due to the superior CR center localization and ease of application, our method has the potential to improve the accuracy and precision of CR-based eye trackers

翻译：我们提出一种深度学习方法，用于在眼部图像中精确定位单个角膜反射（CR）的中心。与以往方法不同，我们使用仅通过仿真数据训练的卷积神经网络（CNN）。仅使用仿真数据的优势在于完全避免了真实眼部图像监督训练所需的手动标注这一耗时过程。为系统评估方法的精度，我们首先在不同背景和噪声水平的仿真CR图像上进行测试，随后利用真实眼部视频数据开展验证。实验表明，在真实眼部图像中，本方法空间精度较现有最优算法提升35%，在仿真图像中其空间准确性达到与现有最优方法相当的水平。结论认为：本方法可实现CR中心的高精度定位，有效解决了数据可用性这一制约眼动追踪深度学习模型开发的常见瓶颈问题。凭借更优的CR中心定位能力与便捷的应用特性，该方法有望提升基于CR的眼动追踪仪器的准确性与精度。