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 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 MASK DETECTION SYSTEM FOR COVID-19 SCENARIO USING COMPUTER VISION

2021 ◽  
Vol 5 (12) ◽  
Author(s):  
Dr. Prakash Prasad ◽  
Mukul Shende ◽  
Mayur Karemore ◽  
Lucky Khobragade ◽  
Amit Dravyakar ◽  
...  
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Detection System ◽  
Face Mask ◽  
World Health ◽  
Public Places ◽  
Learning Module ◽  
Different Types ◽  
Health Organization ◽  
The Impact

The new pandemic of (Coronavirus Disease-2019) COVID-19 continues to spread worldwide. Every potential sector is experiencing a decline in growth. (World Health Organization) WHO suggests that Wearing Face Mask can reduce the impact of COVID-19. So, This Paper Proposed a system that controls the growth of COVID-19 by finding individuals who don't wear masks in populated areas like malls, markets where all public places are under surveillance with closed-circuit television cameras (CCTV). When a person without a mask is found, the corresponding authority is informed by the CCTV network. And it can calculate the number of people that do not wear the mask and emit an audible signal to inform the authority. A deep learning module is trained on a dataset composed of images of people wearing different types of masks and people without masks collected from various sources. It also contains some confusing images that help the model to achieve greater precision than other models. This model will use the dataset to build a COVID-19 face mask detector with computer vision using Computer Vision. This approach allowed extracting even the details from the pixels

scholarly journals Face Mask Detection Using Deep Learning and Computer Vision

2022 ◽  
Vol 10 (1) ◽  
pp. 511-516
Author(s):  
Prod. Roshan R. Kolte
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Virus Transmission ◽  
Face Mask ◽  
Video Stream ◽  
World Health ◽  
Health Crisis ◽  
Protection Method ◽  
The World ◽  
Health Organization

Abstract: COVID-19 pandemic has rapidly affected our day-to-day life the world trade and movements. Wearing a face mask is very essentials for protecting against virus. People also wear mask to cover themselves in order to reduce the spread of covid virus. The corona virus covid-19 pandemic is causing a global health crisis so the effective protection method is wearing a face mask in public area according to the world health organization (WHO). The covid-19 pandemic forced government across the world to impose lockdowns to prevent virus transmission report indicates that wearing face mask while at work clearly reduce the risk of transmission .we will use the dataset to build a covid-19 face mask detector with computer vision using python,opencv,tensorflow,keras library and deep learning. Our goal is to identify whether the person on image or live video stream is wearing mask or not wearing face mask this can help to society and whole organization to avoid the transfer of virus one person to antother.we used computer vision and deep learning modules to detect a with mask image and without mask image. Keywords: face detection, face recognition, CNN, SVM, opencv, python, tensorflow, keras.

scholarly journals A review of studies on the COVID-19 epidemic crisis disease with a preventive approach

Work ◽  
2020 ◽  
Vol 66 (4) ◽  
pp. 717-729 ◽  
Author(s):  
Maryam Feiz Arefi ◽  
Mohsen Poursadeqiyan
Keyword(s):  
Social Distance ◽  
Preventive Measures ◽  
World Health ◽  
Definitive Treatment ◽  
Evidence Based ◽  
Public Places ◽  
Preventive Vaccine ◽  
The World ◽  
Health Organization ◽  
Presence Of People

BACKGROUND: COVID-19 is a highly contagious acute respiratory syndrome and has been declared a pandemic in more than 209 countries worldwide. At the time of writing, no preventive vaccine has been developed and tested in the community. This study was conducted to review studies aimed at preventing the spread of the coronavirus worldwide. METHODS: This study was a review of the evidence-based literature and was conducted by searching databases, including Google Scholar, PubMed, and ScienceDirect, until April 2020. The search was performed based on keywords including “coronavirus”, “COVID-19”, and “prevention”. The list of references in the final studies has also been re-reviewed to find articles that might not have been obtained through the search. The guidelines published by trustworthy organizations such as the World Health Organization and Center for Disease Control have been used in this study. CONCLUSION: So far, no vaccine or definitive treatment for COVID-19 has been invented, and the disease has become a pandemic. Therefore, observation of hand hygiene, disinfection of high-touch surfaces, observation of social distance, and lack of presence in public places are recommended as preventive measures. Moreover, to control the situation and to reduce the incidence of the virus, some of the measures taken by the decision-making bodies and the guidelines of the deterrent institutions to strengthen telecommuting of employees and reduce the presence of people in the community and prevent unnecessary activities, are very important.

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.

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.

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 Efficient Two Stage Identification for Face mask detection using Multiclass Deep Learning Approach

2021 ◽  
Vol 3 (2) ◽  
pp. 107-121
Author(s):  
R Dhaya
Keyword(s):  
Deep Learning ◽  
Binary Classification ◽  
Face Mask ◽  
World Health ◽  
Survey Study ◽  
Identification System ◽  
Two Stage ◽  
Public Places ◽  
The World ◽  
The Face

The World Health Organization (WHO) considers the COVID-19 Coronavirus to be a global pandemic. The most effective form of protection is to wear a face mask in public places. Moreover, the COVID-19 pandemic prompted all the countries to set up a lockdown to prevent viral transmission. According to a survey study, the use of facemasks at work decreases the chances of fast transmission. If the facemasks are not used or are worn incorrectly, it contributes to the third and fourth waves of the corona virus spreading throughout the world. This motivates us to conduct an efficient investigation of the face mask identification system and monitor people, who use suitable face mask in public places. Deep learning is the most effective approach for detecting whether or not a person is wearing a face mask in a crowded area. Using a multiclass deep learning technique, this research study proposes an efficient two stage identification (ETSI) for face mask detection. Whereas, the binary classification does not offer information about face mask detection and error. The proposed approach employs CNN's "ReLU" activation function to detect the face mask. Furthermore, in the current pandemic crisis, this research article offers a very efficient and precise approach for identifying COVID-19. Precision has increased as a result of the employment of a multi-class abbreviation in the final output.

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