  Using the face as a biometric identity trait is motivated by the contactless
nature of the capture process and the high accuracy of the recognition
algorithms. After the current COVID-19 pandemic, wearing a face mask has been
imposed in public places to keep the pandemic under control. However, face
occlusion due to wearing a mask presents an emerging challenge for face
recognition systems. In this paper, we presented a solution to improve the
masked face recognition performance. Specifically, we propose the Embedding
Unmasking Model (EUM) operated on top of existing face recognition models. We
also propose a novel loss function, the Self-restrained Triplet (SRT), which
enabled the EUM to produce embeddings similar to these of unmasked faces of the
same identities. The achieved evaluation results on two face recognition models
and two real masked datasets proved that our proposed approach significantly
improves the performance in most experimental settings.
