  The COVID-19 pandemic has drastically changed accepted norms globally. Within
the past year, masks have been used as a public health response to limit the
spread of the virus. This sudden change has rendered many face recognition
based access control, authentication and surveillance systems ineffective.
Official documents such as passports, driving license and national identity
cards are enrolled with fully uncovered face images. However, in the current
global situation, face matching systems should be able to match these reference
images with masked face images. As an example, in an airport or security
checkpoint it is safer to match the unmasked image of the identifying document
to the masked person rather than asking them to remove the mask. We find that
current facial recognition techniques are not robust to this form of occlusion.
  To address this unique requirement presented due to the current circumstance,
we propose a set of re-purposed datasets and a benchmark for researchers to
use. We also propose a contrastive visual representation learning based
pre-training workflow which is specialized to masked vs unmasked face matching.
We ensure that our method learns robust features to differentiate people across
varying data collection scenarios. We achieve this by training over many
different datasets and validating our result by testing on various holdout
datasets. The specialized weights trained by our method outperform standard
face recognition features for masked to unmasked face matching. We believe the
provided synthetic mask generating code, our novel training approach and the
trained weights from the masked face models will help in adopting existing face
recognition systems to operate in the current global environment. We
open-source all contributions for broader use by the research community.
