ETH-XGaze: A Large Scale Dataset for Gaze Estimation under Extreme Head Pose and Gaze Variation

TL;DR

ETH-XGaze is a large, high-resolution dataset with over one million images capturing extreme head poses and gaze variations, designed to improve and standardize gaze estimation research.

Contribution

The paper introduces ETH-XGaze, a comprehensive dataset with standardized protocols, enabling better evaluation and robustness of gaze estimation methods under challenging conditions.

Findings

Dataset significantly improves gaze estimation robustness.

Standardized protocol facilitates fair comparison.

Benchmark website available for research use.

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