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Authors: Hitoshi Yoshihashi 1 ; Naoto Ienaga 2 and Maki Sugimoto 1

Affiliations: 1 Graduate School of Information and Computer Science, Keio University, Yokohama, Kaganawa, Japan ; 2 Faculty of Engineering, Information and Systems, University of Tsukuba, Tsukuba, Ibaraki, Japan

Keyword(s): Inpainting, Face Completion, Generative Adversarial Networks, Face Mask, COVID-19.

Abstract: In 2020 and beyond, the opportunities to communicate with others while wearing a face mask have increased. A mask hides the mouth and facial muscles, making it difficult to convey facial expressions to others. In this study, we propose to use generative adversarial networks (GAN) to complete the facial region hidden by the mask. We defined custom loss functions that focus on the errors of the feature point coordinates of the face and the pixels in the masked region. As a result, we were able to generate images with higher quality than existing methods.

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Paper citation in several formats:
Yoshihashi, H.; Ienaga, N. and Sugimoto, M. (2022). GAN-based Face Mask Removal using Facial Landmarks and Pixel Errors in Masked Region. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 125-133. DOI: 10.5220/0010827500003124

@conference{visapp22,
author={Hitoshi Yoshihashi. and Naoto Ienaga. and Maki Sugimoto.},
title={GAN-based Face Mask Removal using Facial Landmarks and Pixel Errors in Masked Region},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP},
year={2022},
pages={125-133},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010827500003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP
TI - GAN-based Face Mask Removal using Facial Landmarks and Pixel Errors in Masked Region
SN - 978-989-758-555-5
IS - 2184-4321
AU - Yoshihashi, H.
AU - Ienaga, N.
AU - Sugimoto, M.
PY - 2022
SP - 125
EP - 133
DO - 10.5220/0010827500003124
PB - SciTePress

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