ETH-XGaze: A Large Scale Dataset for Gaze Estimation Under Extreme Head Pose and Gaze Variation
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Author / Producer
Date
2020
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
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Abstract
Gaze estimation is a fundamental task in many applications of computer vision, human computer interaction and robotics. Many state-of-the-art methods are trained and tested on custom datasets, making comparison across methods challenging. Furthermore, existing gaze estimation datasets have limited head pose and gaze variations, and the evaluations are conducted using different protocols and metrics. In this paper, we propose a new gaze estimation dataset called ETH-XGaze, consisting of over one million high-resolution images of varying gaze under extreme head poses. We collect this dataset from 110 participants with a custom hardware setup including 18 digital SLR cameras and adjustable illumination conditions, and a calibrated system to record ground truth gaze targets. We show that our dataset can significantly improve the robustness of gaze estimation methods across different head poses and gaze angles. Additionally, we define a standardized experimental protocol and evaluation metric on ETH-XGaze, to better unify gaze estimation research going forward. The dataset and benchmark website are available at https://ait.ethz.ch/projects/2020/ETH-XGaze.
Permanent link
Publication status
published
External links
Book title
Computer Vision – ECCV 2020
Journal / series
Volume
12350
Pages / Article No.
365 - 381
Publisher
Springer
Event
16th European Conference on Computer Vision (ECCV 2020) (virtual)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
03979 - Hilliges, Otmar (ehemalig) / Hilliges, Otmar (former)
09686 - Tang, Siyu / Tang, Siyu
00002 - ETH Zürich
Notes
Due to the Coronavirus (COVID-19) the conference was conducted virtually.
Funding
717054 - Optimization-based End-User Design of Interactive Technologies (EC)