  In this paper, we address the problem of face recognition with masks. Given
the global health crisis caused by COVID-19, mouth and nose-covering masks have
become an essential everyday-clothing-accessory. This sanitary measure has put
the state-of-the-art face recognition models on the ropes since they have not
been designed to work with masked faces. In addition, the need has arisen for
applications capable of detecting whether the subjects are wearing masks to
control the spread of the virus. To overcome these problems a full training
pipeline is presented based on the ArcFace work, with several modifications for
the backbone and the loss function. From the original face-recognition dataset,
a masked version is generated using data augmentation, and both datasets are
combined during the training process. The selected network, based on ResNet-50,
is modified to also output the probability of mask usage without adding any
computational cost. Furthermore, the ArcFace loss is combined with the
mask-usage classification loss, resulting in a new function named Multi-Task
ArcFace (MTArcFace). Experimental results show that the proposed approach
highly boosts the original model accuracy when dealing with masked faces, while
preserving almost the same accuracy on the original non-masked datasets.
Furthermore, it achieves an average accuracy of 99.78% in mask-usage
classification.
