# Masked Face Recognition using ResNet-50

arXiv: https://arxiv.org/abs/2104.08997 · HTML: https://mlsanity.com/paper/2104.08997/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 30 references that are in the map.
- First posted on arXiv 19 Apr 2021.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Masked Face Recognition Dataset and Application](https://mlsanity.com/paper/2003.09093/) (2020)
- [When Face Recognition Meets with Deep Learning: an Evaluation of Convolutional Neural Networks for Face Recognition](https://mlsanity.com/paper/1504.02351/) (2015)
- [Biometrics Recognition Using Deep Learning: A Survey](https://mlsanity.com/paper/1912.00271/) (2019)
- [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, 5 of 5)

- [Multi-Dataset Benchmarks for Masked Identification using Contrastive Representation Learning](https://mlsanity.com/paper/2106.05596/) (2021)
- [An Explorative Analysis of SVM Classifier and ResNet50 Architecture on African Food Classification](https://mlsanity.com/paper/2505.13923/) (2025)
- [NeuroFlux: Memory-Efficient CNN Training Using Adaptive Local Learning](https://mlsanity.com/paper/2402.14139/) (2024)
- [Co(ve)rtex: ML Models as storage channels and their (mis-)applications](https://mlsanity.com/paper/2307.08811/) (2023)
- [Masked Face Recognition under Different Backbones](https://mlsanity.com/paper/2601.16440/) (2026)

## Abstract (arXiv)

Over the last twenty years, there have seen several outbreaks of different coronavirus diseases across the world. These outbreaks often led to respiratory tract diseases and have proved to be fatal sometimes. Currently, we are facing an elusive health crisis with the emergence of COVID-19 disease of the coronavirus family. One of the modes of transmission of COVID- 19 is airborne transmission. This transmission occurs as humans breathe in the droplets released by an infected person through breathing, speaking, singing, coughing, or sneezing. Hence, public health officials have mandated the use of face masks which can reduce disease transmission by 65%. For face recognition programs, commonly used for security verification purposes, the use of face mask presents an arduous challenge since these programs were typically trained with human faces devoid of masks but now due to the onset of Covid-19 pandemic, they are forced to identify faces with masks. Hence, this paper investigates the same problem by developing a deep learning based model capable of accurately identifying people with face-masks. In this paper, the authors train a ResNet-50 based architecture that performs well at recognizing masked faces. The outcome of this study could be seamlessly integrated into existing face recognition programs that are designed to detect faces for security verification purposes.