Facial Expression Recognition with Quarantine Face Masks Using a Synthetic Dataset Generator

Yücel Çelik, Sezer Gören

2023

Abstract

The usage of face masks has increased dramatically in recent years due to the pandemic. This made many systems that depended on a full facial analysis not as accurate on faces that are covered with a face mask, which may lead to errors in the system. In this paper, we propose a Convolutional Neural Network (CNN) model that was trained solely on face masks to be more accurate and on point, that could more easily determine facial expressions. Our CNN model was trained with a seven different expression category dataset that only had people with face masks. Although we could not find a suitable dataset with face masks, we opted to generate a synthetic one. The dataset generation was done using Python and the help of the OpenCV library. The process is, after finding the dimensions of the face, we Perspective Transform the face mask object to be able to overlay it on the face. After that, the CNN model was also generated using Python with a CNN model. Using this method we gathered favorable results on the test subjects with 70.1% accuracy on the validation batch where previous facial expression recognition systems mostly failed to even recognize the face since they were not trained to recognize faces with face masks.

Paper Citation

in Harvard Style

Çelik Y. and Gören S. (2023). Facial Expression Recognition with Quarantine Face Masks Using a Synthetic Dataset Generator. In Proceedings of the 3rd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE, ISBN 978-989-758-642-2, SciTePress, pages 199-205. DOI: 10.5220/0011992500003497

in Bibtex Style

@conference{improve23,
author={Yücel Çelik and Sezer Gören},
title={Facial Expression Recognition with Quarantine Face Masks Using a Synthetic Dataset Generator},
booktitle={Proceedings of the 3rd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,},
year={2023},
pages={199-205},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011992500003497},
isbn={978-989-758-642-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,
TI - Facial Expression Recognition with Quarantine Face Masks Using a Synthetic Dataset Generator
SN - 978-989-758-642-2
AU - Çelik Y.
AU - Gören S.
PY - 2023
SP - 199
EP - 205
DO - 10.5220/0011992500003497
PB - SciTePress