A survey on computer vision based human analysis in the COVID-19 era

Fevziye Irem Eyiokur*, Alperen Kantarcı, Mustafa Ekrem Erakın, Naser Damer, Ferda Ofli, Muhammad Imran, Janez Križaj, Albert Ali Salah, Alexander Waibel, Vitomir Štruc, Hazım Kemal Ekenel

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks. Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting face-hand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field. Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given at the end of the survey. This work is intended to have a broad appeal and be useful not only for computer vision researchers but also the general public.

Original languageEnglish
Article number104610
JournalImage and Vision Computing
Volume130
DOIs
Publication statusPublished - Feb 2023

Bibliographical note

Publisher Copyright:
© 2022 Elsevier B.V.

Funding

This research was supported in parts by the ARRS Research Programme P2–0250 (B) “Metrology and Biometric Systems” and the additional funding provided for COVID-19 related research as well as the bilateral ARRS-TUBITAK funded project: Low Resolution Face Recognition (FaceLQ) , with TUBITAK project number 120N011 . This research work has been also partially funded by the German Federal Ministry of Education and Research and the Hessen State Ministry for Higher Education, Research and the Arts within their joint support of the National Research Center for Applied Cybersecurity ATHENE , and EU COST project GoodBrother ( 19121 ). The project on which this report is based was also partially funded by the Federal Ministry of Education and Research (BMBF) of Germany under the number 01IS18040A .

FundersFunder number
ARRS-TUBITAK
European Cooperation in Science and Technology19121
Bundesministerium für Bildung und Forschung01IS18040A
Hessisches Ministerium für Wissenschaft und Kunst
Javna Agencija za Raziskovalno Dejavnost RS
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu120N011

    Keywords

    • COVID-19
    • Computer vision
    • Human analysis
    • Masked faces
    • Survey

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