Labeled pupils in the wild: A dataset for studying pupil detection in unconstrained environments
Marc Tonsen, Xucong Zhang, Yusuke Sugano, Andreas Bulling
Proc. ACM International Symposium on Eye Tracking Research and Applications (ETRA), pp. 139-142, 2016.
Abstract
We present labelled pupils in the wild (LPW), a novel dataset of 66 high-quality, high-speed eye region videos for the development and evaluation of pupil detection algorithms. The videos in our dataset were recorded from 22 participants in everyday locations at about 95 FPS using a state-of-the-art dark-pupil head-mounted eye tracker. They cover people of different ethnicities and a diverse set of everyday indoor and outdoor illumination environments, as well as natural gaze direction distributions. The dataset also includes participants wearing glasses, contact lenses, and make-up. We bench- mark five state-of-the-art pupil detection algorithms on our dataset with respect to robustness and accuracy. We further study the influence of image resolution and vision aids as well as recording lo- cation (indoor, outdoor) on pupil detection performance. Our evaluations provide valuable insights into the general pupil detection problem and allow us to identify key challenges for robust pupil detection on head-mounted eye trackers.Links
BibTeX
@inproceedings{tonsen16_etra,
author = {Tonsen, Marc and Zhang, Xucong and Sugano, Yusuke and Bulling, Andreas},
title = {Labeled pupils in the wild: A dataset for studying pupil detection in unconstrained environments},
booktitle = {Proc. ACM International Symposium on Eye Tracking Research and Applications (ETRA)},
year = {2016},
pages = {139-142},
doi = {10.1145/2857491.2857520}
}