  Recent advances in appearance-based models have shown improved eye tracking
performance in difficult scenarios like occlusion due to eyelashes, eyelids or
camera placement, and environmental reflections on the cornea and glasses. The
key reason for the improvement is the accurate and robust identification of eye
parts (pupil, iris, and sclera regions). The improved accuracy often comes at
the cost of labeling an enormous dataset, which is complex and time-consuming.
This work presents two semi-supervised learning frameworks to identify
eye-parts by taking advantage of unlabeled images where labeled datasets are
scarce. With these frameworks, leveraging the domain-specific augmentation and
novel spatially varying transformations for image segmentation, we show
improved performance on various test cases. For instance, for a model trained
on just 48 labeled images, these frameworks achieved an improvement of 0.38%
and 0.65% in segmentation performance over the baseline model, which is trained
only with the labeled dataset.
