COVID-19 Face Mask Recognition with Advanced Face Cut Algorithm for Human Safety Measures

TL;DR

This paper presents a computer vision and deep learning framework for face mask recognition that improves accuracy using a novel face cut algorithm and ResNet50, enhancing safety measures during COVID-19.

Contribution

Introduces a boundary-dependent face cut algorithm combined with ResNet50 for improved mask detection accuracy over existing methods.

Findings

Achieved 3.4% higher accuracy than YOLOV3 in 10 epochs.

Utilized 27 landmarks for precise face segmentation.

Demonstrated effectiveness in real-world mask recognition scenarios.

Abstract

In the last year, the outbreak of COVID-19 has deployed computer vision and machine learning algorithms in various fields to enhance human life interactions. COVID-19 is a highly contaminated disease that affects mainly the respiratory organs of the human body. We must wear a mask in this situation as the virus can be contaminated through the air and a non-masked person can be affected. Our proposal deploys a computer vision and deep learning framework to recognize face masks from images or videos. We have implemented a Boundary dependent face cut recognition algorithm that can cut the face from the image using 27 landmarks and then the preprocessed image can further be sent to the deep learning ResNet50 model. The experimental result shows a significant advancement of 3.4 percent compared to the YOLOV3 mask recognition architecture in just 10 epochs.