scholarly journals How to Correctly Detect Face-Masks for COVID-19 from Visual Information?

2021 ◽  
Vol 11 (5) ◽  
pp. 2070
Author(s):  
Borut Batagelj ◽  
Peter Peer ◽  
Vitomir Štruc ◽  
Simon Dobrišek
Keyword(s):  
Visual Information ◽  
Service Providers ◽  
Face Mask ◽  
World Health ◽  
Detection Techniques ◽  
Face Images ◽  
The World ◽  
Public Behavior ◽  
Face Datasets ◽  
Health Organization

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.

Face mask detection application and dataset

2021 ◽  
Vol 27 (2) ◽  
Author(s):  
Daniel Matthias ◽  
Chidozie Managwu ◽  
O. Olumide
Keyword(s):  
Disease Transmission ◽  
Face Mask ◽  
World Health ◽  
The World ◽  
The Face ◽  
Monitoring And Enforcement ◽  
Health Organization ◽  
Experimental Findings ◽  
Recognition Software ◽  
Deep Learning Model

The COVID–19 pandemic is, without any doubt, changing our world in ways that are beyond our wildest imagination. In a bid to curb the spiraling negative fallouts from the virus that has resulted in a large number of casualties and security concerns. The World Health Organization, amongst other safety protocols, recommended the compulsory wearing of face masks by individuals in public spaces. The problem with the enforcement of this and other relevant safety protocols, all over the world, is the reluctance and outright refusal of citizens to comply and the inability of relevant agencies to monitor and enforce compliance. This paper explores the development of a CCTV–enabled facial mask recognition software that will facilitate the monitoring and enforcement of this protocol. Such models can be particularly useful for security purposes in checking if the disease transmission is being kept in check. A constructive research methodology was adopted, where a pre-trained deep convolutionary neural network (CNN) (mostly eyes and forehead regions) used and the most probable limit (MPL) was use for the classification process. The designed method uses two datasets to train in order to detect key facial features and apply a decision-making algorithm. Experimental findings on the Real-World-Masked-Face-Dataset indicate high success in recognition. A proof of concept as well as a development base are provided towards reducing the spread of COVID-19 by allowing people to validate the face mask via their webcam. We recommend that the use of the app and to further investigate the development of highly robust detectors by training a deep learning model with respect to specified face-feature categories or to correctly and incorrectly wear mask categories.

Stakeholders’ perspectives on adapting the World Health Organization iSupport for Dementia in Australia

Dementia ◽  
2020 ◽  
pp. 147130122095467 ◽  
Author(s):  
Lily D Xiao ◽  
Sue McKechnie ◽  
Lesley Jeffers ◽  
Anita De Bellis ◽  
Elizabeth Beattie ◽  
...  
Keyword(s):  
World Health Organization ◽  
Service Providers ◽  
Care Service ◽  
Informal Caregivers ◽  
Aged Care ◽  
Care Staff ◽  
World Health ◽  
People With Dementia ◽  
The World ◽  
Health Organization

Background In Australia, informal caregivers (family, friends and neighbours) play a crucial role in supporting people with dementia to remain at home. Within the community aged care policy, informal caregivers are acknowledged as assisting with managing care. However, they usually receive very limited dementia care education and training to support them in their role. The World Health Organization (WHO) developed iSupport for Dementia, a comprehensive online dementia education and skill training programme, to address the gap in supporting informal caregivers. Aim The aim of the study was to identify stakeholders’ perspectives regarding adapting the WHO iSupport for use by informal caregivers of people with dementia in Australia. Methods An interpretive description study design was used. Data were collected in focus groups with informal caregivers and care staff of dementia and aged care service providers conducted in May–July 2018. A thematic analysis was utilised to analyse data and identify findings. Results In total, 16 informal caregivers and 20 care staff participated in the study. Five themes were identified. First, informal caregivers perceived iSupport as an opportunity to provide an online one-stop shop to meet their education needs and their needs to manage care services. Second, both informal caregivers and care staff believed that an integrated caregiver network moderated by a health professional was much needed to enable informal caregivers to share learning experiences and enhance social support. Third, both informal caregivers and care staff strongly suggested that dementia and aged care service providers had a role to play in promoting the iSupport. Fourth, informal caregivers were concerned about the time commitment to participate in the iSupport programme. Finally, informal caregivers expected the iSupport to be user-friendly. Conclusion Stakeholders perceived the adaptation of the WHO iSupport in Australia would strengthen informal caregiver education and optimise support for informal caregivers.

scholarly journals Covid-19 Mask Detection

2021 ◽  
Vol 10 (2) ◽  
pp. 1319-1323
Keyword(s):  
Face Mask ◽  
Classification Model ◽  
World Health ◽  
Live Streaming ◽  
Public Places ◽  
The World ◽  
The Face ◽  
Health Organization ◽  
The Right ◽  
Facial Images

The corona epidemic poses a global health problem and therefore effective preventive measures are worn in public places,according to the World Health Organization (WHO). The COVID-19 epidemic has forced governments around the world to impose restrictions on the transmission of the virus. Reports show that wearing the right face while in public places and at work clearly reduces the risk of transmission. An effective and economical way to use machine learning is to create a safe environment for device setup. A hybrid model using the depth of the face mask detection machine will be introduced. The face mask detection databasecontains a mask and in addition to the facial images, we will use OpenCV to perform real-time facial detection from live streaming via our webcam. We will use the database to create a COVID-19 face mask detector from a computer view using Python, OpenCV, and Tensor Flow and Cameras. We aim to determine whether the person in the picture/video is wearing a face mask or not with the help of computer vision and in-depth reading and to show the same with caution. Steps to modeling are data collection, pre-processing, data classification, model testing, and modeling

scholarly journals Face Mask Detection System

2021 ◽  
Vol 9 (VII) ◽  
pp. 1400-1402
Author(s):  
Yatharth Khansali
Keyword(s):  
Real Time ◽  
Detection System ◽  
Face Mask ◽  
World Health ◽  
Public Places ◽  
The World ◽  
The Face ◽  
Time Requirements ◽  
Health Organization ◽  
Protective Mask

COVID-19 pandemic has affected the world severely, according to the World Health Organization (WHO), coronavirus disease (COVID-19) has globally infected over 176 million people causing over 3.8 million deaths. Wearing a protective mask has become a norm. However, it is seen in most public places that people do not wear masks or don’t wear them properly. In this paper, we propose a high accuracy and efficient face mask detector based on MobileNet architecture. The proposed method detects the face in real-time with OpenCV and then identifies if it has a mask on it or not. As a surveillance task, it supports motion, and is trained using transfer learning and compared in terms of both precision and efficiency, with special attention to the real-time requirements of this context.

scholarly journals Autonomic Face Mask Detection with Deep Learning: an IoT Application

2021 ◽  
Author(s):  
◽  
V. H. Benitez-Baltazar
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Face Mask ◽  
World Health ◽  
Image Tracking ◽  
Critical Areas ◽  
Machine Learning Model ◽  
The World ◽  
The Face ◽  
Health Organization

A new and deadly virus known as SARS-CoV-2, which is responsible for the coronavirus disease (COVID-19), is spreading rapidly around the world causing more than 3 million deaths. Hence, there is an urgent need to find new and innovative ways to reduce the likelihood of infection. One of the most common ways of catching the virus is by being in contact with droplets delivered by a sick person. The risk can be reduced by wearing a face mask as suggested by the World Health Organization (WHO), especially in closed environments such as classrooms, hospitals, and supermarkets. However, people hesitate to use a face mask leading to an increase in the risk of spreading the disease, moreover when the face mask is used, sometimes it is worn in the wrong way. In this work, an autonomic face mask detection system with deep learning and powered by the image tracking technique used for the augmented reality development is proposed as a mechanism to request the correct use of face masks to grant access to people to critical areas. To achieve this, a machine learning model based on Convolutional Neural Networks was built on top of an IoT framework to enforce the correct use of the face mask in required areas as it is requested by law in some regions.

scholarly journals Bare Face Person Recognition System using Deep Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 329-337
Author(s):  
Prachi Satpute
Keyword(s):  
Deep Learning ◽  
Public Space ◽  
Service Providers ◽  
Virus Disease ◽  
Face Mask ◽  
Control Measures ◽  
World Health ◽  
Camera System ◽  
Simplified Approach ◽  
The World

Nowadays, maintaining a good hygiene is very important to prevent many diseases like Corona Virus Disease (COVID-19). It has been rapidly affected our day-today life by disrupting the world trade and movements. The World Health Organization (WHO) recommend to the world that all people must wear a mask to prevent COVID-19. The use of masks is part of a comprehensive package of prevention and control measures that can limit the spread of certain respiratory viral diseases. Wearing a protective mask has become a new normal and beneficial for human being to avoid certain diseases. In the near future, many public service providers will ask the customers to wear the masks to provide their services. Therefore, face mask detection has become an important task to help global society. This paper introduce a simplified approach for face mask detection by using Deep learning and python as the programming language. We are also using Open-CV, to search for faces within a picture and then identifies if it has a mask on it or not. By using this system, the surveillance camera system present at some public Space will automatically detect whether the persons are wearing a mask or not.

scholarly journals Aerosol blocking assessment by different types of fabrics for homemade respiratory masks: spectroscopy and imaging study

2020 ◽  
Author(s):  
Mauricio Foschini ◽  
ADAMO FG MONTE ◽  
Ana CM Mendes ◽  
Renata J Scarabucci ◽  
Alexandre Maletta ◽  
...  
Keyword(s):  
Imaging Study ◽  
Face Mask ◽  
World Health ◽  
Reference Standard ◽  
Lack Of Information ◽  
Batch Fabrication ◽  
Aerosol Scattering ◽  
The World ◽  
Different Types ◽  
Health Organization

During the COVID-19 pandemic, there is no agreement, until the current date, about the recommendations of homemade face mask use for the general population, and one of the reasons is a lack of information about their real protective rule on spreading aerosols and viruses. This is a comparative study regarding the relative efficiencies of commercial respiratory masks (medical masks) and homemade fabric masks, which may guide authorities across the globe, following the 'Advice on the use of masks in the context of COVID-19', by the World Health Organization. We described two optical methodologies for charactering respiratory masks. It happens that the aerosol scattering coefficient is linear as a function of its concentration inside the mask chamber. Quantitative optical properties of scattering for a large batch fabrication of masks were demonstrated, making the mask N95 suitable for use as a reference standard.

scholarly journals Community universal face mask use during the COVID 19 pandemic—from households to travellers and public spaces

2020 ◽  
Vol 27 (3) ◽  
Author(s):  
C Raina MacIntyre ◽  
S Jay Hasanain
Keyword(s):  
Disease Control ◽  
World Health Organization ◽  
Face Mask ◽  
Public Spaces ◽  
World Health ◽  
Community Settings ◽  
Community Members ◽  
The Us ◽  
The World ◽  
Health Organization

As the COVID-19 pandemic grows globally, universal face mask use (UFMU) has become a topic of discussion, with a recommendation made from the US Centers for Disease Control (CDC) for cloth mask use by community members. Other countries and the World Health Organization advise against UFMU. We outline the rationale and evidence supporting UFMU in households, during travel and in crowded public spaces in high transmission community settings.

scholarly journals A Review on COVID-19 Face Mask Detection using CNN

2021 ◽  
pp. 39-45
Author(s):  
Kavita R. Singh ◽  
Shailesh D. Kamble ◽  
Samiksha M. Kalbande ◽  
Punit Fulzele
Keyword(s):  
Computer Vision ◽  
Face Mask ◽  
World Health ◽  
Effective Protection ◽  
Protection Measure ◽  
Public Places ◽  
The World ◽  
Secure Environment ◽  
Health Organization ◽  
Corona Viruses

The World Health Organization claims (WHO),Corona Viruses the COVID-19 pandemic is causing a nationwide crisis, wearing a mask on a face in public places is an effective protection measure. The COVID-19 pandemic forced governments all over the world to implement quarantine measures in order to deter virus spread. Reports suggest that the risk of transmission is clearly minimized by wearing face masks when at work. An effective and economic approach to the use of AI in a manufacturing setting to build a secure environment. Using a face mask detection dataset, we will use Open CV to perform real-time face detection from a live stream from our webcam. Using Keras, Python, Tensorflow and Open CV, and, it will build a COVID-19 face mask detector with computer vision. Using computer vision and CNN, I aim to decide whether or not the person in the image or video streaming is wear a mask.

scholarly journals A Comparative Analysis of Variant Deep Learning Models for COVID-19 Protective Face Mask Detection

2021 ◽  
Vol 12 (6) ◽  
pp. 2841-2848
Author(s):  
Rohan Katari Et al.
Keyword(s):  
Deep Learning ◽  
Comparative Analysis ◽  
Disease Transmission ◽  
Detection System ◽  
Face Mask ◽  
Public Spaces ◽  
World Health ◽  
Learning Models ◽  
The World ◽  
Health Organization

The world is in the midst of a paramount pandemic owing to the rapid dissemination of coronavirus disease (COVID-19) brought about by the spread of the virus ‘SARS-CoV-2’. It is mainly transmitted among persons through airborne diffusion of droplets containing the virus produced by an infected person sneezing or coughing without covering their face. The World Health Organization (WHO) has issued numerous guidelines which state that the spread of this disease can be limited by people shielding their faces with protective face masks when in public or in crowded areas. As a precautionary measure, many nations have implemented obligations for face mask usage in public spaces. But manual monitoring of huge crowds in public spaces for face masks is laborious. Hence, this requires the development of an automated face mask detection system using deep learning models and related technologies. The detection system should be viable and deployable in real-time, predicting the result accurately so as to be used by monitoring bodies to ensure that the face mask guidelines are followed by the public thereby preventing the disease transmission. In this paper we aim to perform a comparative analysis of various sophisticated image classifiers based on deep learning, in terms of vital metrics of performance to identify the effective deep learning based model for face mask detection.

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