The emergence of the global COVID-19 pandemic poses new challenges for
biometrics. Not only are contactless biometric identification options becoming
more important, but face recognition has also recently been confronted with the
frequent wearing of masks. These masks affect the performance of previous face
recognition systems, as they hide important identity information. In this
paper, we propose a mask-invariant face recognition solution (MaskInv) that
utilizes template-level knowledge distillation within a training paradigm that
aims at producing embeddings of masked faces that are similar to those of
non-masked faces of the same identities. In addition to the distilled
knowledge, the student network benefits from additional guidance by
margin-based identity classification loss, ElasticFace, using masked and
non-masked faces. In a step-wise ablation study on two real masked face
databases and five mainstream databases with synthetic masks, we prove the
rationalization of our MaskInv approach. Our proposed solution outperforms
previous state-of-the-art (SOTA) academic solutions in the recent MFRC-21
challenge in both scenarios, masked vs masked and masked vs non-masked, and
also outperforms the previous solution on the MFR2 dataset. Furthermore, we
demonstrate that the proposed model can still perform well on unmasked faces
with only a minor loss in verification performance. The code, the trained
models, as well as the evaluation protocol on the synthetically masked data are
publicly available: this https URL

本文提出了一个掩码不变的面部识别解决方案 (MaskInv)，它利用模板级知识蒸馏和基于边距的身份分类损失 ElasticFace 来对具有遮罩的面孔进行嵌入学习。在两个真实的有遮罩人脸数据库和五个主流数据库以及合成遮罩的数据集上的逐步去除研究表明，该方法优于之前的 SOTA 方案，在最近的 MFRC-21 挑战赛中具有较高的性能，且只有较小的验证性能损失。