Predicting Eye Gaze Location on Websites

Ciheng Zhang, Decky Aspandi, Steffen Staab, Steffen Staab

2023

Abstract

World-Wide-Web, with website and webpage as a main interface, facilitates dissemination of important information. Hence it is crucial to optimize webpage design for better user interaction, which is primarily done by analyzing users’ behavior, especially users’ eye-gaze locations on the webpage. However, gathering these data is still considered to be labor and time intensive. In this work, we enable the development of automatic eye-gaze estimations given webpage screenshots as input by curating of a unified dataset that consists of webpage screenshots, eye-gaze heatmap and website’s layout information in the form of image and text masks. Our curated dataset allows us to propose a deep learning-based model that leverages on both webpage screenshot and content information (image and text spatial location), which are then combined through attention mechanism for effective eye-gaze prediction. In our experiment, we show benefits of careful fine-tuning using our unified dataset to improve accuracy of eye-gaze predictions. We further observe the capability of our model to focus on targeted areas (images and text) to achieve accurate eye-gaze area predictions. Finally, comparison with other alternatives shows state-of-the-art result of our approach, establishing a benchmark for webpage based eye-gaze prediction task.

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Paper Citation


in Harvard Style

Zhang C., Aspandi D. and Staab S. (2023). Predicting Eye Gaze Location on Websites. 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 121-132. DOI: 10.5220/0011747300003417


in Bibtex Style

@conference{visapp23,
author={Ciheng Zhang and Decky Aspandi and Steffen Staab},
title={Predicting Eye Gaze Location on Websites},
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={121-132},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011747300003417},
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 - Predicting Eye Gaze Location on Websites
SN - 978-989-758-634-7
AU - Zhang C.
AU - Aspandi D.
AU - Staab S.
PY - 2023
SP - 121
EP - 132
DO - 10.5220/0011747300003417
PB - SciTePress