# Multi-Stage CNN Architecture for Face Mask Detection

arXiv: https://arxiv.org/abs/2009.07627 · HTML: https://mlsanity.com/paper/2009.07627/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2020.

## Built on by (most cited first, 5 of 5)

- [A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19](https://mlsanity.com/paper/2201.04777/) (2022)
- [An Improved Lightweight YOLOv5 Model Based on Attention Mechanism for Face Mask Detection](https://mlsanity.com/paper/2203.16506/) (2022)
- [A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking](https://mlsanity.com/paper/2405.05900/) (2024)
- [Automated Thermal Screening for COVID-19 using Machine Learning](https://mlsanity.com/paper/2203.14128/) (2022)
- [Human Behavior in the Time of COVID-19: Learning from Big Data](https://mlsanity.com/paper/2303.13452/) (2023)

## Abstract (arXiv)

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.