A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
Mohamed Mahmoud, Mahmoud SalahEldin Kasem, Hyun-Soo Kang

TL;DR
This survey comprehensively reviews the challenges, advancements, and deep learning methodologies in masked face recognition, detection, and unmasking, highlighting datasets, evaluation metrics, and future research directions in this evolving field.
Contribution
It provides an extensive analysis of deep learning-based approaches, benchmark datasets, and evaluation metrics for masked face recognition and unmasking, offering valuable insights for future research.
Findings
Deep learning techniques have significantly advanced masked face recognition.
Benchmark datasets and metrics are crucial for evaluating MFR performance.
Challenges remain in developing robust systems for fully occluded faces.
Abstract
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…
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Taxonomy
TopicsFace recognition and analysis
MethodsMeta Face Recognition
