Download file
Download file
2 files

A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19

posted on 2022-01-19, 22:57 authored by Bingshu WangBingshu Wang, C.L. Philip Chen, Jiangbin Zheng
Coronavirus disease 2019 (COVID-19) continues to pose a great challenge to the world since its outbreak. To fight against the disease, a series of artificial intelligence (AI) techniques are developed and applied to real-world scenarios such as safety monitoring, disease diagnosis, infection risk assessment, lesion segmentation of COVID-19 CT scans, etc. The coronavirus epidemics have forced people wear masks to counteract the transmission of virus, which also brings difficulties to monitor large groups of people wearing masks. In this paper, we primarily focus on the AI techniques of masked facial detection and related datasets. We survey the recent advances, beginning with the descriptions of masked facial detection datasets. Thirteen available datasets are described and discussed in details. Then, the methods are roughly categorized into two classes: conventional methods and neural network-based methods. Conventional methods are usually trained by boosting algorithms with handcrafted features, which accounts for a small proportion. Neural network-based methods are further classified as three parts according to the number of processing stages. Representative algorithms are described in detail, coupled with some typical techniques that are described briefly. Finally, we summarize the recent benchmarking results, give the discussions on the limitations of datasets and methods, and expand future research directions. To our knowledge, this is the first survey about masked facial detection methods and datasets. Hopefully our survey could provide some help to fight against epidemics.


National Natural Science Foundation of China, Youth Fund under number 62102318

Fundamental Research Funds for the Central Universities under number G2020KY05113

National Key Research and Development Program of China under number 2019YFA0706200 and 2019YFB1703600

National Natural Science Foundation of China grant under number 61702195, 61751202, U1813203, U1801262, 61751205

Science and Technology Major Project of Guangzhou under number 202007030006


Email Address of Submitting Author

ORCID of Submitting Author


Submitting Author's Institution

Northwestern Polytechnical University

Submitting Author's Country

  • China