# MaskedFace-Net -- A Dataset of Correctly/Incorrectly Masked Face Images in the Context of COVID-19

arXiv: https://arxiv.org/abs/2008.08016 · HTML: https://mlsanity.com/paper/2008.08016/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2020.

## Built on by (most cited first, 30 of 31)

- [Boosting Masked Face Recognition with Multi-Task ArcFace](https://mlsanity.com/paper/2104.09874/) (2021)
- [Detection of a facemask in real-time using deep learning methods: Prevention of Covid 19](https://mlsanity.com/paper/2401.15675/) (2024)
- [MLFW: A Database for Face Recognition on Masked Faces](https://mlsanity.com/paper/2109.05804/) (2021)
- [BinaryCoP: Binary Neural Network-based COVID-19 Face-Mask Wear and Positioning Predictor on Edge Devices](https://mlsanity.com/paper/2102.03456/) (2021)
- [Extract-Transform-Load for Video Streams](https://mlsanity.com/paper/2310.04830/) (2023)
- [Unconstrained Face-Mask & Face-Hand Datasets: Building a Computer Vision System to Help Prevent the Transmission of COVID-19](https://mlsanity.com/paper/2103.08773/) (2021)
- [A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19](https://mlsanity.com/paper/2201.04777/) (2022)
- [Longitudinal Analysis of Mask and No-Mask on Child Face Recognition](https://mlsanity.com/paper/2111.00121/) (2021)
- [Indian Masked Faces in the Wild Dataset](https://mlsanity.com/paper/2106.09670/) (2021)
- [SD-GAN: Semantic Decomposition for Face Image Synthesis with Discrete Attribute](https://mlsanity.com/paper/2207.05300/) (2022)
- [Bias-Aware Face Mask Detection Dataset](https://mlsanity.com/paper/2211.01207/) (2022)
- [A Survey on Computer Vision based Human Analysis in the COVID-19 Era](https://mlsanity.com/paper/2211.03705/) (2022)
- [Relevant Irrelevance: Generating Alterfactual Explanations for Image Classifiers](https://mlsanity.com/paper/2405.05295/) (2024)
- [A 3D model-based approach for fitting masks to faces in the wild](https://mlsanity.com/paper/2103.00803/) (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)
- [Real-time Face Mask Detection in Video Data](https://mlsanity.com/paper/2105.01816/) (2021)
- [Mask Usage Recognition using Vision Transformer with Transfer Learning and Data Augmentation](https://mlsanity.com/paper/2203.11542/) (2022)
- [A transfer learning approach with convolutional neural network for Face Mask Detection](https://mlsanity.com/paper/2310.18928/) (2023)
- [A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking](https://mlsanity.com/paper/2405.05900/) (2024)
- [A Comparative Analysis of Machine Learning Approaches for Automated Face Mask Detection During COVID-19](https://mlsanity.com/paper/2112.07913/) (2021)
- [Biometrics in the Time of Pandemic: 40% Masked Face Recognition Degradation can be Reduced to 2%](https://mlsanity.com/paper/2201.00461/) (2022)
- [A Masked Face Classification Benchmark on Low-Resolution Surveillance Images](https://mlsanity.com/paper/2211.13061/) (2022)
- [Learning Representations for Masked Facial Recovery](https://mlsanity.com/paper/2212.14110/) (2022)
- [HeMPPCAT: Mixtures of Probabilistic Principal Component Analysers for Data with Heteroscedastic Noise](https://mlsanity.com/paper/2301.08852/) (2023)
- [Human Behavior in the Time of COVID-19: Learning from Big Data](https://mlsanity.com/paper/2303.13452/) (2023)
- [Wearing face mask detection using deep learning through COVID-19 pandemic](https://mlsanity.com/paper/2305.00068/) (2023)
- [A Comparative Study of Face Detection Algorithms for Masked Face Detection](https://mlsanity.com/paper/2305.11077/) (2023)
- [Revealing an Unattractivity Bias in Mental Reconstruction of Occluded Faces using Generative Image Models](https://mlsanity.com/paper/2409.15443/) (2024)
- [ViQA-COVID: COVID-19 Machine Reading Comprehension Dataset for Vietnamese](https://mlsanity.com/paper/2504.21017/) (2025)
- [Two-Factor Authentication Smart Entryway Using Modified LBPH Algorithm](https://mlsanity.com/paper/2508.13617/) (2025)

## Abstract (arXiv)

The wearing of the face masks appears as a solution for limiting the spread of COVID-19. In this context, efficient recognition systems are expected for checking that people faces are masked in regulated areas. To perform this task, a large dataset of masked faces is necessary for training deep learning models towards detecting people wearing masks and those not wearing masks. Some large datasets of masked faces are available in the literature. However, at the moment, there are no available large dataset of masked face images that permits to check if detected masked faces are correctly worn or not. Indeed, many people are not correctly wearing their masks due to bad practices, bad behaviors or vulnerability of individuals (e.g., children, old people). For these reasons, several mask wearing campaigns intend to sensitize people about this problem and good practices. In this sense, this work proposes three types of masked face detection dataset; namely, the Correctly Masked Face Dataset (CMFD), the Incorrectly Masked Face Dataset (IMFD) and their combination for the global masked face detection (MaskedFace-Net). Realistic masked face datasets are proposed with a twofold objective: i) to detect people having their faces masked or not masked, ii) to detect faces having their masks correctly worn or incorrectly worn (e.g.; at airport portals or in crowds). To the best of our knowledge, no large dataset of masked faces provides such a granularity of classification towards permitting mask wearing analysis. Moreover, this work globally presents the applied mask-to-face deformable model for permitting the generation of other masked face images, notably with specific masks. Our datasets of masked face images (137,016 images) are available at https://github.com/cabani/MaskedFace-Net.