  With the recent world-wide COVID-19 pandemic, using face masks have become an
important part of our lives. People are encouraged to cover their faces when in
public area to avoid the spread of infection. The use of these face masks has
raised a serious question on the accuracy of the facial recognition system used
for tracking school/office attendance and to unlock phones. Many organizations
use facial recognition as a means of authentication and have already developed
the necessary datasets in-house to be able to deploy such a system.
Unfortunately, masked faces make it difficult to be detected and recognized,
thereby threatening to make the in-house datasets invalid and making such
facial recognition systems inoperable. This paper addresses a methodology to
use the current facial datasets by augmenting it with tools that enable masked
faces to be recognized with low false-positive rates and high overall accuracy,
without requiring the user dataset to be recreated by taking new pictures for
authentication. We present an open-source tool, MaskTheFace to mask faces
effectively creating a large dataset of masked faces. The dataset generated
with this tool is then used towards training an effective facial recognition
system with target accuracy for masked faces. We report an increase of 38% in
the true positive rate for the Facenet system. We also test the accuracy of
re-trained system on a custom real-world dataset MFR2 and report similar
accuracy.
