Masked Face Recognition using ResNet-50

TL;DR

This paper develops a ResNet-50 based deep learning model to improve face recognition accuracy for masked faces, addressing challenges posed by COVID-19 mask mandates.

Contribution

The paper introduces a ResNet-50 based architecture specifically trained to recognize faces with masks, enhancing security verification systems during the pandemic.

Findings

ResNet-50 model achieves high accuracy on masked face recognition

The approach can be integrated into existing face recognition systems

Improves security verification under mask-wearing conditions

Abstract

Over the last twenty years, there have seen several outbreaks of different coronavirus diseases across the world. These outbreaks often led to respiratory tract diseases and have proved to be fatal sometimes. Currently, we are facing an elusive health crisis with the emergence of COVID-19 disease of the coronavirus family. One of the modes of transmission of COVID- 19 is airborne transmission. This transmission occurs as humans breathe in the droplets released by an infected person through breathing, speaking, singing, coughing, or sneezing. Hence, public health officials have mandated the use of face masks which can reduce disease transmission by 65%. For face recognition programs, commonly used for security verification purposes, the use of face mask presents an arduous challenge since these programs were typically trained with human faces devoid of masks but now due to the onset of Covid-19 pandemic, they are forced to identify faces with masks. Hence, this paper investigates the same problem by developing a deep learning based model capable of accurately identifying people with face-masks. In this paper, the authors train a ResNet-50 based architecture that performs well at recognizing masked faces. The outcome of this study could be seamlessly integrated into existing face recognition programs that are designed to detect faces for security verification purposes.