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：我们提出了一种基于深度学习的眼图角膜反射（CR）中心精准定位方法。与现有方法不同，本方法采用仅基于模拟数据训练的卷积神经网络（CNN）。仅使用模拟数据的优势在于完全规避了基于真实眼图进行监督训练所需的手工标注耗时过程。为系统评估所提方法的精度，我们首先在具有不同背景噪声水平的模拟CR图像上进行测试，随后在真实人眼采集的高质量视频中开展验证。实验结果表明：在真实眼图上，本方法的空间定位精度较当前最优算法提升35%；在模拟图像上的空间准确度与现有最优方法持平。研究证实本方法能够实现高精度的CR中心定位，同时有效解决了开发深度学习视线估计模型时普遍面临的数据可用性这一关键瓶颈。凭借卓越的CR中心定位性能与简便的部署特性，本方法有望显著提升基于CR的眼动追踪仪的测量准确度与精度。