MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation
journal contribution
posted on 2023-11-29, 18:07authored byXucong Zhang, Yusuke Sugano, Mario FritzMario Fritz, Andreas Bulling
Learning-based methods are believed to work well for unconstrained gaze estimation, i.e. gaze estimation from a
monocular RGB camera without assumptions regarding user, environment, or camera. However, current gaze datasets were collected
under laboratory conditions and methods were not evaluated across multiple datasets. Our work makes three contributions towards
addressing these limitations. First, we present the MPIIGaze dataset, which contains 213,659 full face images and corresponding
ground-truth gaze positions collected from 15 users during everyday laptop use over several months. An experience sampling
approach ensured continuous gaze and head poses and realistic variation in eye appearance and illumination. To facilitate
cross-dataset evaluations, 37,667 images were manually annotated with eye corners, mouth corners, and pupil centres. Second, we
present an extensive evaluation of state-of-the-art gaze estimation methods on three current datasets, including MPIIGaze. We study
key challenges including target gaze range, illumination conditions, and facial appearance variation. We show that image resolution
and the use of both eyes affect gaze estimation performance, while head pose and pupil centre information are less informative. Finally,
we propose GazeNet, the first deep appearance-based gaze estimation method. GazeNet improves on the state of the art by 22%
(from a mean error of 13.9 degrees to 10.8 degrees) for the most challenging cross-dataset evaluation.
History
Preferred Citation
Xucong Zhang, Yusuke Sugano, Mario Fritz and Andreas Bulling. MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation. In: IEEE Transactions on Pattern Analysis and Machine Intelligence. 2019.
Primary Research Area
Algorithmic Foundations and Cryptography
Secondary Research Area
Secure Connected and Mobile Systems
Legacy Posted Date
2020-01-12
Journal
IEEE Transactions on Pattern Analysis and Machine Intelligence
Open Access Type
Unknown
Sub Type
Article
BibTeX
@article{cispa_all_3014,
title = "MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation",
author = "Zhang, Xucong and Sugano, Yusuke and Fritz, Mario and Bulling, Andreas",
journal="{IEEE Transactions on Pattern Analysis and Machine Intelligence}",
year="2019",
}