2023 |
Eyiokur, Fevziye Irem; Kantarci, Alperen; Erakin, Mustafa Ekrem; Damer, Naser; Ofli, Ferda; Imran, Muhammad; Križaj, Janez; Salah, Albert Ali; Waibel, Alexander; Štruc, Vitomir; Ekenel, Hazim K. A Survey on Computer Vision based Human Analysis in the COVID-19 Era Članek v strokovni reviji V: Image and Vision Computing, vol. 130, no. 104610, str. 1-19, 2023. Povzetek | Povezava | BibTeX | Oznake: COVID-19, face, face alignment, face analysis, face image processing, face image quality assessment, face landmarking, face recognition, face verification, human analysis, masked face analysis @article{IVC2023, 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. |
2021 |
Batagelj, Borut; Peer, Peter; Štruc, Vitomir; Dobrišek, Simon How to correctly detect face-masks for COVID-19 from visual information? Članek v strokovni reviji V: Applied sciences, vol. 11, no. 5, str. 1-24, 2021, ISBN: 2076-3417. Povzetek | Povezava | BibTeX | Oznake: computer vision, COVID-19, deep learning, detection, face, mask detection, recognition @article{Batagelj2021, The new Coronavirus disease (COVID-19) has seriously affected the world. By the end of November 2020, the global number of new coronavirus cases had already exceeded 60 million and the number of deaths 1,410,378 according to information from the World Health Organization (WHO). To limit the spread of the disease, mandatory face-mask rules are now becoming common in public settings around the world. Additionally, many public service providers require customers to wear face-masks in accordance with predefined rules (e.g., covering both mouth and nose) when using public services. These developments inspired research into automatic (computer-vision-based) techniques for face-mask detection that can help monitor public behavior and contribute towards constraining the COVID-19 pandemic. Although existing research in this area resulted in efficient techniques for face-mask detection, these usually operate under the assumption that modern face detectors provide perfect detection performance (even for masked faces) and that the main goal of the techniques is to detect the presence of face-masks only. In this study, we revisit these common assumptions and explore the following research questions: (i) How well do existing face detectors perform with masked-face images? (ii) Is it possible to detect a proper (regulation-compliant) placement of facial masks? and (iii) How useful are existing face-mask detection techniques for monitoring applications during the COVID-19 pandemic? To answer these and related questions we conduct a comprehensive experimental evaluation of several recent face detectors for their performance with masked-face images. Furthermore, we investigate the usefulness of multiple off-the-shelf deep-learning models for recognizing correct face-mask placement. Finally, we design a complete pipeline for recognizing whether face-masks are worn correctly or not and compare the performance of the pipeline with standard face-mask detection models from the literature. To facilitate the study, we compile a large dataset of facial images from the publicly available MAFA and Wider Face datasets and annotate it with compliant and non-compliant labels. The annotation dataset, called Face-Mask-Label Dataset (FMLD), is made publicly available to the research community. |
Objave
2023 |
A Survey on Computer Vision based Human Analysis in the COVID-19 Era Članek v strokovni reviji V: Image and Vision Computing, vol. 130, no. 104610, str. 1-19, 2023. |
2021 |
How to correctly detect face-masks for COVID-19 from visual information? Članek v strokovni reviji V: Applied sciences, vol. 11, no. 5, str. 1-24, 2021, ISBN: 2076-3417. |