  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.
