# Efficient Masked Face Recognition Method during the COVID-19 Pandemic

arXiv: https://arxiv.org/abs/2105.03026 · HTML: https://mlsanity.com/paper/2105.03026/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 43 references that are in the map.
- First posted on arXiv 7 May 2021.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [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)
- [Occlusion Robust Face Recognition Based on Mask Learning with PairwiseDifferential Siamese Network](https://mlsanity.com/paper/1908.06290/) (2019)
- [Learning Bag-of-Features Pooling for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1707.08105/) (2017)
- [Robust 3D face recognition in presence of pose and partial occlusions or missing parts](https://mlsanity.com/paper/1408.3709/) (2014)

## Built on by (most cited first, 16 of 16)

- [WebFace260M: A Benchmark for Million-Scale Deep Face Recognition](https://mlsanity.com/paper/2204.10149/) (2022)
- [Masked Face Recognition Challenge: The InsightFace Track Report](https://mlsanity.com/paper/2108.08191/) (2021)
- [Masked Face Recognition Challenge: The WebFace260M Track Report](https://mlsanity.com/paper/2108.07189/) (2021)
- [Periocular Biometrics and its Relevance to Partially Masked Faces: A Survey](https://mlsanity.com/paper/2203.15203/) (2022)
- [A novel Facial Recognition technique with Focusing on Masked Faces](https://mlsanity.com/paper/2501.04444/) (2025)
- [Multi-Dataset Benchmarks for Masked Identification using Contrastive Representation Learning](https://mlsanity.com/paper/2106.05596/) (2021)
- [HiMFR: A Hybrid Masked Face Recognition Through Face Inpainting](https://mlsanity.com/paper/2209.08930/) (2022)
- [Balanced Masked and Standard Face Recognition](https://mlsanity.com/paper/2110.01521/) (2021)
- [Does a Face Mask Protect my Privacy?: Deep Learning to Predict Protected Attributes from Masked Face Images](https://mlsanity.com/paper/2112.07879/) (2021)
- [Ensemble Learning using Transformers and Convolutional Networks for Masked Face Recognition](https://mlsanity.com/paper/2210.04816/) (2022)
- [Towards Large-scale Masked Face Recognition](https://mlsanity.com/paper/2310.16364/) (2023)
- [A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking](https://mlsanity.com/paper/2405.05900/) (2024)
- [A Comparative Study of Face Detection Algorithms for Masked Face Detection](https://mlsanity.com/paper/2305.11077/) (2023)
- [Masked Face Dataset Generation and Masked Face Recognition](https://mlsanity.com/paper/2311.07475/) (2023)
- [PLGSA-Transformer: Periocular Landmark-Guided Attention with Occlusion-Adaptive Cosine Thresholding for Cross-Modal Masked and Unmasked Face Recognition](https://mlsanity.com/paper/2607.03581/) (2026)
- [A Comparative Transfer-Learning Study of CNN Backbones for Partial Face Recognition on the SoF Dataset](https://mlsanity.com/paper/2609.31677/) (2026)

## Abstract (arXiv)

The coronavirus disease (COVID-19) is an unparalleled crisis leading to a huge number of casualties and security problems. In order to reduce the spread of coronavirus, people often wear masks to protect themselves. This makes face recognition a very difficult task since certain parts of the face are hidden. A primary focus of researchers during the ongoing coronavirus pandemic is to come up with suggestions to handle this problem through rapid and efficient solutions. In this paper, we propose a reliable method based on occlusion removal and deep learning-based features in order to address the problem of the masked face recognition process. The first step is to remove the masked face region. Next, we apply three pre-trained deep Convolutional Neural Networks (CNN) namely, VGG-16, AlexNet, and ResNet-50, and use them to extract deep features from the obtained regions (mostly eyes and forehead regions). The Bag-of-features paradigm is then applied to the feature maps of the last convolutional layer in order to quantize them and to get a slight representation comparing to the fully connected layer of classical CNN. Finally, Multilayer Perceptron (MLP) is applied for the classification process. Experimental results on Real-World-Masked-Face-Dataset show high recognition performance compared to other state-of-the-art methods.