How to Correctly Detect Face-Masks for COVID-19 from Visual Information?
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 o...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/5/2070 |
| _version_ | 1827588262425788416 |
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| author | Borut Batagelj Peter Peer Vitomir Štruc Simon Dobrišek |
| author_facet | Borut Batagelj Peter Peer Vitomir Štruc Simon Dobrišek |
| author_sort | Borut Batagelj |
| collection | DOAJ |
| description | 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 <i>the presence</i> 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 <i>compliant</i> and <i>non-compliant</i> labels. The <i>annotation dataset</i>, called Face-Mask-Label Dataset (FMLD), is made publicly available to the research community. |
| first_indexed | 2024-03-09T00:30:59Z |
| format | Article |
| id | doaj.art-dfa2ddeef06f4b05b4959b2188fdfe3c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T00:30:59Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-dfa2ddeef06f4b05b4959b2188fdfe3c2023-12-11T18:31:32ZengMDPI AGApplied Sciences2076-34172021-02-01115207010.3390/app11052070How to Correctly Detect Face-Masks for COVID-19 from Visual Information?Borut Batagelj0Peter Peer1Vitomir Štruc2Simon Dobrišek3Faculty of Computer and Information Science, University of Ljubljana, Večna Pot 113, SI-1000 Ljubljana, SloveniaFaculty of Computer and Information Science, University of Ljubljana, Večna Pot 113, SI-1000 Ljubljana, SloveniaFaculty of Electrical Engineering, University of Ljubljana, Tržaška Cesta 25, SI-1000 Ljubljana, SloveniaFaculty of Electrical Engineering, University of Ljubljana, Tržaška Cesta 25, SI-1000 Ljubljana, SloveniaThe 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 <i>the presence</i> 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 <i>compliant</i> and <i>non-compliant</i> labels. The <i>annotation dataset</i>, called Face-Mask-Label Dataset (FMLD), is made publicly available to the research community.https://www.mdpi.com/2076-3417/11/5/2070COVID-19masked-face detectionface-mask classificationface-mask recognitionCOVID-19 compliant mask detection |
| spellingShingle | Borut Batagelj Peter Peer Vitomir Štruc Simon Dobrišek How to Correctly Detect Face-Masks for COVID-19 from Visual Information? Applied Sciences COVID-19 masked-face detection face-mask classification face-mask recognition COVID-19 compliant mask detection |
| title | How to Correctly Detect Face-Masks for COVID-19 from Visual Information? |
| title_full | How to Correctly Detect Face-Masks for COVID-19 from Visual Information? |
| title_fullStr | How to Correctly Detect Face-Masks for COVID-19 from Visual Information? |
| title_full_unstemmed | How to Correctly Detect Face-Masks for COVID-19 from Visual Information? |
| title_short | How to Correctly Detect Face-Masks for COVID-19 from Visual Information? |
| title_sort | how to correctly detect face masks for covid 19 from visual information |
| topic | COVID-19 masked-face detection face-mask classification face-mask recognition COVID-19 compliant mask detection |
| url | https://www.mdpi.com/2076-3417/11/5/2070 |
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