Multi-Stage CNN Architecture for Face Mask Detection

TL;DR

This paper presents a dual-stage CNN system designed to automatically detect face mask usage in real-time CCTV footage, aiding in enforcing safety protocols during and after the COVID-19 pandemic.

Contribution

It introduces a novel multi-stage CNN architecture specifically for face mask detection, capable of integration with existing surveillance systems.

Findings

High accuracy in mask detection demonstrated

Effective integration with CCTV cameras shown

Potential to reduce safety violations

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.