Multi-Stage CNN Architecture for Face Mask Detection
This addresses the need for automated monitoring of face mask compliance in public or workplace settings, though it is incremental as it builds on existing CNN methods for detection tasks.
The paper tackles the problem of manually tracking face mask usage during the COVID-19 pandemic by introducing a dual-stage CNN architecture that detects masked and unmasked faces, which can be integrated with CCTV cameras to help track safety violations and ensure a safe environment.
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.