scholarly journals Real-Time Face Mask Detection using Deep Learning

2021 ◽  
Vol 12 (1) ◽  
pp. 25-31
Author(s):  
Pranad Munjal ◽  
Vikas Rattan ◽  
Rajat Dua ◽  
Varun Malik
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Face Mask ◽  
World Health ◽  
Infected Person ◽  
Accuracy Rate ◽  
Public Areas ◽  
Health Organization ◽  
Respiratory Droplets ◽  
Real Time Application

The outbreak of COVID-19 has taught everyone the importance of face masks in their lives. SARS-COV-2(Severe Acute Respiratory Syndrome) is a communicable virus that is transmitted from a person while speaking, sneezing in the form of respiratory droplets. It spreads by touching an infected surface or by being in contact with an infected person. Healthcare officials from the World Health Organization and local authorities are propelling people to wear face masks as it is one of the comprehensive strategies to overcome the transmission. Amid the advancement of technology, deep learning and computer vision have proved to be an effective way in recognition through image processing. This system is a real-time application to detect people if they are wearing a mask or are without a mask. It has been trained with the dataset that contains around 4000 images using 224x224 as width and height of the image and have achieved an accuracy rate of 98%. In this research, this model has been trained and compiled with 2 CNN for differentiating accuracy to choose the best for this type of model.It can be put into action in public areas such as airports, railways, schools, offices, etc. to check if COVID-19 guidelines are being adhered to or not.

scholarly journals IoT and Deep Learning Based Approach for Rapid Screening and Face Mask Detection for Infection Spread Control of COVID-19

2021 ◽  
Vol 11 (8) ◽  
pp. 3495
Author(s):  
Shabir Hussain ◽  
Yang Yu ◽  
Muhammad Ayoub ◽  
Akmal Khan ◽  
Rukhshanda Rehman ◽  
...  
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Face Mask ◽  
World Health ◽  
Rapid Screening ◽  
Screening System ◽  
Source Of Infection ◽  
The Face ◽  
Health Organization ◽  
Vaccine Distribution

The spread of COVID-19 has been taken on pandemic magnitudes and has already spread over 200 countries in a few months. In this time of emergency of COVID-19, especially when there is still a need to follow the precautions and developed vaccines are not available to all the developing countries in the first phase of vaccine distribution, the virus is spreading rapidly through direct and indirect contacts. The World Health Organization (WHO) provides the standard recommendations on preventing the spread of COVID-19 and the importance of face masks for protection from the virus. The excessive use of manual disinfection systems has also become a source of infection. That is why this research aims to design and develop a low-cost, rapid, scalable, and effective virus spread control and screening system to minimize the chances and risk of spread of COVID-19. We proposed an IoT-based Smart Screening and Disinfection Walkthrough Gate (SSDWG) for all public places entrance. The SSDWG is designed to do rapid screening, including temperature measuring using a contact-free sensor and storing the record of the suspected individual for further control and monitoring. Our proposed IoT-based screening system also implemented real-time deep learning models for face mask detection and classification. This module classified individuals who wear the face mask properly, improperly, and without a face mask using VGG-16, MobileNetV2, Inception v3, ResNet-50, and CNN using a transfer learning approach. We achieved the highest accuracy of 99.81% while using VGG-16 and the second highest accuracy of 99.6% using MobileNetV2 in the mask detection and classification module. We also implemented classification to classify the types of face masks worn by the individuals, either N-95 or surgical masks. We also compared the results of our proposed system with state-of-the-art methods, and we highly suggested that our system could be used to prevent the spread of local transmission and reduce the chances of human carriers of COVID-19.

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 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 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.

scholarly journals Compliance and Etiquettes of Using Face Masks in Public during COVID-19 Pandemic: A Community-Based Survey from Islamabad, Pakistan

2021 ◽  
Vol 11 (2) ◽  
pp. 79-86
Author(s):  
Mishaal Fazal ◽  
Talha Khawar ◽  
Zawar Raza ◽  
Laiba Maryam ◽  
Arshia Bilal ◽  
...  
Keyword(s):  
General Public ◽  
Statistical Tests ◽  
National Policy ◽  
Face Mask ◽  
World Health ◽  
Sample Population ◽  
Cross Sectional ◽  
Public Areas ◽  
Study Population ◽  
Health Organization

Background: To determine the frequency of general public compliant to face mask and also to evaluate knowledge and practice of using face mask in accordance with World Health Organization (WHO) and National Institute of Health (NIH) guidelines in our community. Methods: A cross-sectional descriptive design was adopted. A sample of 190 people was recruited from public areas in Islamabad during the COVID-19 pandemic. The study was conducted over a period of two months between November and December 2020. Knowledge and etiquettes of face mask use were evaluated with the help of a self-developed questionnaire based on WHO and NIH guidelines. Statistical tests were applied to understand the association of knowledge with etiquettes and also to see the effects of demographic variables on them. Results: This study revealed that one fourth of the sample population was not wearing face masks in public. More than half were not concerned about COVID-19 pandemic and only one third knew about the national policy related to face mask usage. Only one-tenth of the population wore the correct type of mask recommended for general public use (cloth mask). The participant’s performance in the etiquette aspect of using face mask was also poor. Conclusion: The general public is unaware of Pakistan’s national policy on face masks. Although majority of the study population was compliant with wearing masks in public, results were unsatisfactory in terms of their etiquette. Effort is needed to increase the general public’s concern in value of using face mask and awareness about correct techniques.

scholarly journals Spartan Face Mask Detection and Facial Recognition System

Healthcare ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 87
Author(s):  
Ziwei Song ◽  
Kristie Nguyen ◽  
Tien Nguyen ◽  
Catherine Cho ◽  
Jerry Gao
Keyword(s):  
Deep Learning ◽  
Face Detection ◽  
Facial Recognition ◽  
Learning Algorithms ◽  
Face Mask ◽  
Recognition System ◽  
World Health ◽  
Identity Recognition ◽  
Facial Recognition System ◽  
Health Organization

According to the World Health Organization (WHO), wearing a face mask is one of the most effective protections from airborne infectious diseases such as COVID-19. Since the spread of COVID-19, infected countries have been enforcing strict mask regulation for indoor businesses and public spaces. While wearing a mask is a requirement, the position and type of the mask should also be considered in order to increase the effectiveness of face masks, especially at specific public locations. However, this makes it difficult for conventional facial recognition technology to identify individuals for security checks. To solve this problem, the Spartan Face Detection and Facial Recognition System with stacking ensemble deep learning algorithms is proposed to cover four major issues: Mask Detection, Mask Type Classification, Mask Position Classification and Identity Recognition. CNN, AlexNet, VGG16, and Facial Recognition Pipeline with FaceNet are the Deep Learning algorithms used to classify the features in each scenario. This system is powered by five components including training platform, server, supporting frameworks, hardware, and user interface. Complete unit tests, use cases, and results analytics are used to evaluate and monitor the performance of the system. The system provides cost-efficient face detection and facial recognition with masks solutions for enterprises and schools that can be easily applied on edge-devices.

scholarly journals Low Cost System for Face Mask Detection Based Haar Cascade Classifier Method

2021 ◽  
Vol 21 (1) ◽  
pp. 21-30
Author(s):  
Radimas Putra Muhammad Davi Labib ◽  
Sirojul Hadi ◽  
Parama Diptya Widayaka
Keyword(s):  
Detection System ◽  
Low Cost ◽  
Face Mask ◽  
World Health ◽  
Cascade Classifier ◽  
The World ◽  
Public Areas ◽  
The Face ◽  
Haar Cascade ◽  
Health Organization

In December 2019, there was a pandemic caused by a new type of coronavirus, namely SARS-CoV-2 (Severe Acute Respiratory Syndrome Corona Virus 2) spread almost throughout the world. The World Health Organization (WHO) named it COVID-19 (Coronavirus Disease). To minimize the spread of the COVID-19, the Indonesian government announced a policy for the social distancing of 1-2 meters and wearing a medical mask. In this study, a mask detection system was built using the Haar Cascade Classifier method by detecting the facial areas such as the nose and lips. The study aims to distinguish between using masks and on the contrary. It is expected that the mask detection system can be implemented to provide direct warnings to people who do not wear masks in public areas. The results using the Haar Cascade Classifier method show that the system designed is able to detect faces, noses, and lips at a light intensity of 80-140 lux. The face is detected at a distance of 30-120cm, while the nose is at a distance of 30-60cm, while the lips are at a distance of 30-70cm. The system designed can perform the detection process at a speed of 5 fps. The overall test results obtained a success rate of 88,89%.

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.

Coronavirus diseases and pregnancy: COVID-19,SARS, and MERS

2020 ◽  
pp. 276-289
Author(s):  
Mobina Fathi ◽  
Kimia Vakili ◽  
Niloofar Deravi
Keyword(s):  
Pregnant Women ◽  
Intrauterine Growth Retardation ◽  
Negative Impact ◽  
World Health ◽  
Wuhan City ◽  
Intrauterine Growth ◽  
Exponential Increase ◽  
The World ◽  
Health Organization ◽  
Respiratory Droplets

Around the end of December 2019, a new beta-coronavirus from Wuhan City, Hubei Province, China began to spread rapidly. The new virus, called SARS-CoV-2, which could be transmitted through respiratory droplets, had a range of mild to severe symptoms, from simple cold in some cases to death in others. The disease caused by SARS-CoV-2 was named COVID-19 by WHO and has so far killed more people than SARS and MERS. Following the widespread global outbreak of COVID-19, with more than 132758 confirmed cases and 4955 deaths worldwide, the World Health Organization declared COVID-19 a pandemic disease in January 2020. Earlier studies on viral pneumonia epidemics has shown that pregnant women are at greater risk than others. During pregnancy, the pregnant woman is more prone to infectious diseases. Research on both SARS-CoV and MERS-CoV, which are pathologically similar to SARS-CoV-2, has shown that being infected with these viruses during pregnancy increases the risk of maternal death, stillbirth, intrauterine growth retardation and, preterm delivery. With the exponential increase in cases of COVID-19 throughout the world, there is a need to understand the effects of SARS-CoV-2 on the health of pregnant women, through extrapolation of earlier studies that have been conducted on pregnant women infected with SARS-CoV, and MERS-CoV. There is an urgent need to understand the chance of vertical transmission of SARS-CoV-2 from mother to fetus and the possibility of the virus crossing the placental barrier. Additionally, since some viral diseases and antiviral drugs may have a negative impact on the mother and fetus, in which case, pregnant women need special attention for the prevention, diagnosis, and treatment of COVID-19.

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