Face-Based Gaze Estimation Using Residual Attention Pooling Network

Chaitanya Bandi, Ulrike Thomas

2023

Abstract

Gaze estimation reveals a person’s intent and willingness to interact, which is an important cue in human-robot interaction applications to gain a robot’s attention. With tremendous developments in deep learning architectures and easily accessible cameras, human eye gaze estimation has received a lot of attention. Compared to traditional model-based gaze estimation methods, appearance-based methods have shown a substantial improvement in accuracy. In this work, we present an appearance-based gaze estimation architecture that adopts convolutions, residuals, and attention blocks to increase gaze accuracy further. Face and eye images are generally adopted separately or in combination for the estimation of eye gaze. In this work, we rely entirely on facial features, since the gaze can be tracked under extreme head pose variations. With the proposed architecture, we attain better than state-of-the-art accuracy on the MPIIFaceGaze dataset and the ETH-XGaze open-source benchmark.

Paper Citation

in Harvard Style

Bandi C. and Thomas U. (2023). Face-Based Gaze Estimation Using Residual Attention Pooling Network. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 541-549. DOI: 10.5220/0011789200003417

in Bibtex Style

@conference{visapp23,
author={Chaitanya Bandi and Ulrike Thomas},
title={Face-Based Gaze Estimation Using Residual Attention Pooling Network},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={541-549},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011789200003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - Face-Based Gaze Estimation Using Residual Attention Pooling Network
SN - 978-989-758-634-7
AU - Bandi C.
AU - Thomas U.
PY - 2023
SP - 541
EP - 549
DO - 10.5220/0011789200003417
PB - SciTePress