  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
