  We present a large scale data set, OpenEDS: Open Eye Dataset, of eye-images
captured using a virtual-reality (VR) head mounted display mounted with two
synchronized eyefacing cameras at a frame rate of 200 Hz under controlled
illumination. This dataset is compiled from video capture of the eye-region
collected from 152 individual participants and is divided into four subsets:
(i) 12,759 images with pixel-level annotations for key eye-regions: iris, pupil
and sclera (ii) 252,690 unlabelled eye-images, (iii) 91,200 frames from
randomly selected video sequence of 1.5 seconds in duration and (iv) 143 pairs
of left and right point cloud data compiled from corneal topography of eye
regions collected from a subset, 143 out of 152, participants in the study. A
baseline experiment has been evaluated on OpenEDS for the task of semantic
segmentation of pupil, iris, sclera and background, with the mean
intersectionover-union (mIoU) of 98.3 %. We anticipate that OpenEDS will create
opportunities to researchers in the eye tracking community and the broader
machine learning and computer vision community to advance the state of
eye-tracking for VR applications. The dataset is available for download upon
request at https://research.fb.com/programs/openeds-challenge
