OpenEDS: Open Eye Dataset

TL;DR

OpenEDS is a comprehensive large-scale eye image dataset captured with VR headsets, enabling advancements in eye-tracking and computer vision through annotated images, unlabelled data, video sequences, and 3D eye scans.

Contribution

The paper introduces OpenEDS, a novel, extensive eye dataset with diverse data types and annotations, facilitating research in eye-tracking and related fields.

Findings

Baseline semantic segmentation achieved 98.3% mIoU.

Dataset includes over 12,000 annotated images and 252,690 unlabelled images.

Provides multi-modal data including point clouds for comprehensive analysis.

Abstract

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