  The pandemic of these very recent years has led to a dramatic increase in
people wearing protective masks in public venues. This poses obvious challenges
to the pervasive use of face recognition technology that now is suffering a
decline in performance. One way to address the problem is to revert to face
recovery methods as a preprocessing step. Current approaches to face
reconstruction and manipulation leverage the ability to model the face
manifold, but tend to be generic. We introduce a method that is specific for
the recovery of the face image from an image of the same individual wearing a
mask. We do so by designing a specialized GAN inversion method, based on an
appropriate set of losses for learning an unmasking encoder. With extensive
experiments, we show that the approach is effective at unmasking face images.
In addition, we also show that the identity information is preserved
sufficiently well to improve face verification performance based on several
face recognition benchmark datasets.
