CycleGAN-based Approach for Masked Face Classification

Tomoya Matsubara, Ahmed Moustafa

2022

Abstract

In this paper, we propose a learning model for not only distinguishing whether a person is wearing masks but also classifying the position of the worn masks (mask on my chin, mask on my chin and mouth). First, the synthesized face masks image dataset used for training the model is generated closer to the real world data by CycleGAN. Then, the presence / absence and position of masks are classified using a machine learning model. Experimental results show that this approach provides excellent performance in classifying the presence/ absence and the position of masks.

Paper Citation

in Harvard Style

Matsubara T. and Moustafa A. (2022). CycleGAN-based Approach for Masked Face Classification. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 476-483. DOI: 10.5220/0010844100003116

in Bibtex Style

@conference{icaart22,
author={Tomoya Matsubara and Ahmed Moustafa},
title={CycleGAN-based Approach for Masked Face Classification},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={476-483},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010844100003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - CycleGAN-based Approach for Masked Face Classification
SN - 978-989-758-547-0
AU - Matsubara T.
AU - Moustafa A.
PY - 2022
SP - 476
EP - 483
DO - 10.5220/0010844100003116