  Eye image segmentation is a critical step in eye tracking that has great
influence over the final gaze estimate. Segmentation models trained using
supervised machine learning can excel at this task, their effectiveness is
determined by the degree of overlap between the narrow distributions of image
properties defined by the target dataset and highly specific training datasets,
of which there are few. Attempts to broaden the distribution of existing eye
image datasets through the inclusion of synthetic eye images have found that a
model trained on synthetic images will often fail to generalize back to
real-world eye images. In remedy, we use dimensionality-reduction techniques to
measure the overlap between the target eye images and synthetic training data,
and to prune the training dataset in a manner that maximizes distribution
overlap. We demonstrate that our methods result in robust, improved performance
when tackling the discrepancy between simulation and real-world data samples.
