  We present a novel multistream network that learns robust eye representations
for gaze estimation. We first create a synthetic dataset containing eye region
masks detailing the visible eyeball and iris using a simulator. We then perform
eye region segmentation with a U-Net type model which we later use to generate
eye region masks for real-world eye images. Next, we pretrain an eye image
encoder in the real domain with self-supervised contrastive learning to learn
generalized eye representations. Finally, this pretrained eye encoder, along
with two additional encoders for visible eyeball region and iris, are used in
parallel in our multistream framework to extract salient features for gaze
estimation from real-world images. We demonstrate the performance of our method
on the EYEDIAP dataset in two different evaluation settings and achieve
state-of-the-art results, outperforming all the existing benchmarks on this
dataset. We also conduct additional experiments to validate the robustness of
our self-supervised network with respect to different amounts of labeled data
used for training.
