  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 present a solution to improve 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 three face recognition models,
two real masked datasets, and two synthetically generated masked face datasets
proved that our proposed approach significantly improves the performance in
most experimental settings.
