Masked face recognition (MFR) has emerged as a critical domain in biometric
identification, especially by the global COVID-19 pandemic, which introduced
widespread face masks. This survey paper presents a comprehensive analysis of
the challenges and advancements in recognising and detecting individuals with
masked faces, which has seen innovative shifts due to the necessity of adapting
to new societal norms. Advanced through deep learning techniques, MFR, along
with Face Mask Recognition (FMR) and Face Unmasking (FU), represent significant
areas of focus. These methods address unique challenges posed by obscured
facial features, from fully to partially covered faces. Our comprehensive
review delves into the various deep learning-based methodologies developed for
MFR, FMR, and FU, highlighting their distinctive challenges and the solutions
proposed to overcome them. Additionally, we explore benchmark datasets and
evaluation metrics specifically tailored for assessing performance in MFR
research. The survey also discusses the substantial obstacles still facing
researchers in this field and proposes future directions for the ongoing
development of more robust and effective masked face recognition systems. This
paper serves as an invaluable resource for researchers and practitioners,
offering insights into the evolving landscape of face recognition technologies
in the face of global health crises and beyond.

面罩脸部识别（MFR）是生物识别中的关键领域，尤其是全球 COVID-19 疫情导致广泛戴口罩。本综述论文对具有面罩的个体识别和检测中的挑战和进展进行了全面分析，这一领域由于需要适应新的社会规范而发生了创新的变化。通过深度学习技术的先进和面蒙面识别（FMR）以及面部去蒙面（FU）代表着重要的研究领域。这些方法解决了由完全到部分遮挡面部特征所带来的独特挑战。我们综合审查了针对 MFR、FMR 和 FU 开发的各种基于深度学习的方法，突出了它们的独特挑战和应对方法。此外，我们还探讨了专门用于评估 MFR 研究性能的基准数据集和评估指标。该综述还讨论了研究人员在这一领域面临的重大障碍，并提出了未来进一步发展更稳健有效的面罩脸部识别系统的方向。本文为研究人员和从业者提供了宝贵的资源，洞察了面对全球卫生危机及其后果时面部识别技术的演变景观。