Multi-Stage CNN Architecture for Face Mask Detection
Abstract
The end of 2019 witnessed the outbreak of Coronavirus Disease 2019 (COVID-19), which has continued to be the cause of plight for millions of lives and businesses even in 2020. As the world recovers from the pandemic and plans to return to a state of normalcy, there is a wave of anxiety among all individuals, especially those who intend to resume in-person activity. Studies have proved that wearing a face mask significantly reduces the risk of viral transmission as well as provides a sense of protection. However, it is not feasible to manually track the implementation of this policy. Technology holds the key here. We introduce a Deep Learning based system that can detect instances where face masks are not used properly. Our system consists of a dual-stage Convolutional Neural Network (CNN) architecture capable of detecting masked and unmasked faces and can be integrated with pre-installed CCTV cameras. This will help track safety violations, promote the use of face masks, and ensure a safe working environment.
Keywords
Cite
@article{arxiv.2009.07627,
title = {Multi-Stage CNN Architecture for Face Mask Detection},
author = {Amit Chavda and Jason Dsouza and Sumeet Badgujar and Ankit Damani},
journal= {arXiv preprint arXiv:2009.07627},
year = {2020}
}