CycleGAN-based Approach for Masked Face Classification

Tomoya Matsubara; Ahmed Moustafa

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Paper

CycleGAN-based Approach for Masked Face Classification

Topics: Deep Learning; Neural Networks

In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, 476-483, 2022

Authors: Tomoya Matsubara and Ahmed Moustafa

Affiliation: Nagoya Institute of Technology, Nagoya, Japan

Keyword(s): Machine Learning, Image, Pattern Recognition.

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.

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Paper citation in several formats:

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; ISSN 2184-433X, SciTePress, pages 476-483. DOI: 10.5220/0010844100003116

@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},
issn={2184-433X},
}

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
IS - 2184-433X
AU - Matsubara, T.
AU - Moustafa, A.
PY - 2022
SP - 476
EP - 483
DO - 10.5220/0010844100003116
PB - SciTePress