  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.
