# Inclusive Review on Advances in Masked Human Face Recognition Technologies

arXiv: https://arxiv.org/abs/2508.00841 · HTML: https://mlsanity.com/paper/2508.00841/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 80 references that are in the map.
- First posted on arXiv 4 Jul 2025.

## Builds on (most cited first, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Learning Face Representation from Scratch](https://mlsanity.com/paper/1411.7923/) (2014)
- [Masked Face Recognition Dataset and Application](https://mlsanity.com/paper/2003.09093/) (2020)
- [Cross-Quality LFW: A Database for Analyzing Cross-Resolution Image Face Recognition in Unconstrained Environments](https://mlsanity.com/paper/2108.10290/) (2021)
- [Learning Face Age Progression: A Pyramid Architecture of GANs](https://mlsanity.com/paper/1711.10352/) (2017)
- [MorphGAN: One-Shot Face Synthesis GAN for Detecting Recognition Bias](https://mlsanity.com/paper/2012.05225/) (2020)
- [Anti-Makeup: Learning A Bi-Level Adversarial Network for Makeup-Invariant Face Verification](https://mlsanity.com/paper/1709.03654/) (2017)
- [Indian Masked Faces in the Wild Dataset](https://mlsanity.com/paper/2106.09670/) (2021)
- [Bias-Aware Face Mask Detection Dataset](https://mlsanity.com/paper/2211.01207/) (2022)
- [An Exploratory Study of Masked Face Recognition with Machine Learning Algorithms](https://mlsanity.com/paper/2306.08549/) (2023)
- [Look Through Masks: Towards Masked Face Recognition with De-Occlusion Distillation](https://mlsanity.com/paper/2409.12385/) (2024)
- [False Discovery Rate Computation: Illustrations and Modifications](https://mlsanity.com/paper/2010.04680/) (2020)
- [Age Gap Reducer-GAN for Recognizing Age-Separated Faces](https://mlsanity.com/paper/2011.05897/) (2020)
- [A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking](https://mlsanity.com/paper/2405.05900/) (2024)

## Abstract (arXiv)

Masked Face Recognition (MFR) is an increasingly important area in biometric recognition technologies, especially with the widespread use of masks as a result of the COVID-19 pandemic. This development has created new challenges for facial recognition systems due to the partial concealment of basic facial features. This paper aims to provide a comprehensive review of the latest developments in the field, with a focus on deep learning techniques, especially convolutional neural networks (CNNs) and twin networks (Siamese networks), which have played a pivotal role in improving the accuracy of covering face recognition. The paper discusses the most prominent challenges, which include changes in lighting, different facial positions, partial concealment, and the impact of mask types on the performance of systems. It also reviews advanced technologies developed to overcome these challenges, including data enhancement using artificial databases and multimedia methods to improve the ability of systems to generalize. In addition, the paper highlights advance in deep network design, feature extraction techniques, evaluation criteria, and data sets used in this area. Moreover, it reviews the various applications of masked face recognition in the fields of security and medicine, highlighting the growing importance of these systems in light of recurrent health crises and increasing security threats. Finally, the paper focuses on future research trends such as developing more efficient algorithms and integrating multimedia technologies to improve the performance of recognition systems in real-world environments and expand their applications.