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Authors: Ivan George L. Tarun 1 ; Vidal Wyatt M. Lopez 1 ; Patricia Angela R. Abu 1 and Ma. Regina Justina E. Estuar 2

Affiliations: 1 Ateneo Laboratory for Intelligent Visual Environment, Dept. of Information Systems and Computer Science, Ateneo De Manila University, Katipunan Avenue, Quezon City, Philippines ; 2 Ateneo Center for Computing Competency and Research,Ateneo De Manila University, Katipunan Avenue, Quezon City, Philippines

Keyword(s): Face Mask Detection, Object Detection, Deep Learning, Computer Vision.

Abstract: One such protocol currently enforced by the Philippine government to combat COVID-19 is the mandatory use of face masks in public places. The problem however is that ensuring people follow this protocol is difficult to monitor during a pandemic due to other conflicting health protocols like social distancing and workforce reduction. This study therefore explores on the creation of deep learning models that consider both frontal and side view images of the face for face mask detection. In doing so, improvements to robustness were found when compared to using models that were previously trained on purely frontal images. This was accomplished by first relabeling a subset of images from the FMLD dataset. These images were then split into train, validation, and test sets. Four deep learning models (YOLOv5 Small, YOLOv5 Medium, CenterNet Resnet50 V1 FPN 512x512, CenterNet HourGlass104 512x512) were later trained on the training set of images. These four models were compared with three mode ls (MobileNetV1, ResNet50, VGG16) that were trained previously on purely frontal images. Results show that the four models trained on the relabeled FMLD dataset offer an approximately 20% increase in classification accuracy over the three models that were previously trained on purely frontal images. (More)

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Paper citation in several formats:
Tarun, I.; Lopez, V.; Abu, P. and Estuar, M. (2022). Robust Face Mask Detection with Combined Frontal and Angled Viewed Faces. In Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-569-2; ISSN 2184-4992, SciTePress, pages 462-470. DOI: 10.5220/0010986000003179

@conference{iceis22,
author={Ivan George L. Tarun. and Vidal Wyatt M. Lopez. and Patricia Angela R. Abu. and Ma. Regina Justina E. Estuar.},
title={Robust Face Mask Detection with Combined Frontal and Angled Viewed Faces},
booktitle={Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2022},
pages={462-470},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010986000003179},
isbn={978-989-758-569-2},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Robust Face Mask Detection with Combined Frontal and Angled Viewed Faces
SN - 978-989-758-569-2
IS - 2184-4992
AU - Tarun, I.
AU - Lopez, V.
AU - Abu, P.
AU - Estuar, M.
PY - 2022
SP - 462
EP - 470
DO - 10.5220/0010986000003179
PB - SciTePress