Abstract
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
Abstract (translated)
我们提出了一种深度学习方法，用于准确地定位眼睛图像中的单个角膜反射(CR)的中心。与以前的教学方法不同，我们使用了卷积神经网络(CNN)，该网络仅使用模拟数据进行训练。仅使用模拟数据的好处是完全避免了在真实眼睛图像上进行手动标注所需的繁琐过程。为了系统地评估我们的方法的准确性，我们首先测试了在不同背景上放置的模拟CR和嵌入不同水平的噪声的图像。其次，我们测试了从真实眼睛中捕获的高质量的视频。我们的方法在真实眼睛图像上比最先进的算法方法表现更好，在空间精度方面下降了35%。在模拟图像方面，我们的方法和最先进的方法在空间精度上表现相似。我们的结论是，我们的方法提供了一种精确的CR中心定位方法，并为解决数据可用性问题提供了解决方案，这个问题是深度学习模型 gaze估计开发中的一个重要障碍。由于我们的方法优异的CR中心定位能力和简单易用，它可能有潜力改进基于CR的眼睛跟踪器的精度和精度。
URL
https://arxiv.org/abs/2304.05673
