  The recent Covid-19 pandemic and the fact that wearing masks in public is now
mandatory in several countries, created challenges in the use of face
recognition systems (FRS). In this work, we address the challenge of masked
face recognition (MFR) and focus on evaluating the verification performance in
FRS when verifying masked vs unmasked faces compared to verifying only unmasked
faces. We propose a methodology that combines the traditional triplet loss and
the mean squared error (MSE) intending to improve the robustness of an MFR
system in the masked-unmasked comparison mode. The results obtained by our
proposed method show improvements in a detailed step-wise ablation study. The
conducted study showed significant performance gains induced by our proposed
training paradigm and modified triplet loss on two evaluation databases.
