Few-Shot Gaze Estimation via Gaze Transfer

Nikolaos Poulopoulos, Emmanouil Psarakis

2023

Abstract

Precise gaze estimation constitutes a challenging problem in many computer vision applications due to many limitations related to the great variability of human eye shapes, facial expressions and orientations as well as the illumination variations and the presence of occlusions. Nowadays, the increasing interest of deep neural networks requires a great amount of training data. However, the dependency on labeled data for the purpose of gaze estimation constitutes a significant issue because they are expensive to obtain and require dedicated hardware setup. To address these issues, we introduce a few-shot learning approach which exploits a large amount of unlabeled data to disentangle the gaze feature and train a gaze estimator using only few calibration samples. This is achieved by performing gaze transfer between image pairs that share similar eye appearance but different gaze information via the joint training of a gaze estimation and a gaze transfer network. Thus, the gaze estimation network learns to disentangle the gaze feature indirectly in order to perform precisely the gaze transfer task. Experiments on two publicly available datasets reveal promising results and enhanced accuracy against other few-shot gaze estimation methods.

Paper Citation

in Harvard Style

Poulopoulos N. and Psarakis E. (2023). Few-Shot Gaze Estimation via Gaze Transfer. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 806-813. DOI: 10.5220/0011789800003417

in Bibtex Style

@conference{visapp23,
author={Nikolaos Poulopoulos and Emmanouil Psarakis},
title={Few-Shot Gaze Estimation via Gaze Transfer},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP},
year={2023},
pages={806-813},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011789800003417},
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 5: VISAPP
TI - Few-Shot Gaze Estimation via Gaze Transfer
SN - 978-989-758-634-7
AU - Poulopoulos N.
AU - Psarakis E.
PY - 2023
SP - 806
EP - 813
DO - 10.5220/0011789800003417
PB - SciTePress