A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking

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 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.