scholarly journals Face Mask Detection Using Deep Learning and Computer Vision

Prod. Roshan R. Kolte

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.

D. Gayatri Devi

The corona virus COVID-19 pandemic is causing a global health crisis so the effective protection method is wearing a face mask and maintaining social distance in public areas according to the World Health Organization (WHO). The COVID-19 pandemic forced governments across the world to impose lockdowns to prevent virus transmissions. Reports Indicate that wearing facemasks and maintaining social distance while at work clearly reduces the risk of transmission. An efficient and economic approach of using AI to create a safe environment in a manufacturing setup. So we are doing a Project on detecting whether a person wears a mask or not, also giving an alert message to the person to wear a mask, and maintain social distance or not. A hybrid model using deep and classical machine learning for face mask detection will be presented for face mask detection, a face recognition model is used to identify faces and an object detection algorithm is used to identify persons and also calculate social distance between each other. We collected face mask detection dataset consisting of mask and without mask images, and person photos to identify the person. We are going to use OpenCV to do real-time face detection from a live stream via our webcam. We will use the dataset to build a COVID-19 face mask detector with computer vision using Python, OpenCV, and TensorFlow and Keras, use a face recognition module to identify faces and a YOLO algorithm to detect objects and calculate social distance. Our goal is to identify whether the person on a video stream is wearing a face mask or not, if not give an alert message to wear a mask and check for social distance between each other with the help of computer vision and deep learning.

2021 ◽  
V. H. Benitez-Baltazar

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.

Dr. Prakash Prasad ◽  
Mukul Shende ◽  
Mayur Karemore ◽  
Lucky Khobragade ◽  
Amit Dravyakar ◽  

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

Kavita R. Singh ◽  
Shailesh D. Kamble ◽  
Samiksha M. Kalbande ◽  
Punit Fulzele

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.

Rohan Katari Et al.

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.

2021 ◽  
Vinita Sankla ◽  
Savitanandan Patidar ◽  
Vishal kushwaha ◽  
Ashish Bagwari ◽  
Rahul Tiwari ◽  

Abstract COVID-19 is one of the most dangerous viruses which caused a pandemic in human life, not only in terms of direct casualties but also regarding socio-economic impact. The outbreak quickly spread around the world. The 1st anniversary of the global Corona virus pandemic gets passed away in 2021, but still, no way to tell how long the pandemic will continue. After analyzing a report by WHO of covid-19, to minimize the rate of covid-19 transmission, our national government advised citizens to wear face masks. A model using deep learning and MobileNetV2 for face mask detection is presented. This method was trained and checked on the real-time dataset. There are 3,833 images in the Medical Masks Dataset, including 1918 images of people wearing no mask and 1915 images of people wearing masks. We adopted OpenCV to detect faces in real-time from a live stream captured with our webcam. With the aid of computer vision and deep learning, we hope to classify whether or not the person in the video stream is wearing a face mask. If the camera captures a face without a mask an Email notification will be sent out to the administrator and the system alarm will ring.

2021 ◽  
Vol 11 (8) ◽  
pp. 3495
Shabir Hussain ◽  
Yang Yu ◽  
Muhammad Ayoub ◽  
Akmal Khan ◽  
Rukhshanda Rehman ◽  

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.

2020 ◽  
Vol 13 (1) ◽  
Nafisa Qibriya Khan ◽  
A. H. Farooqui ◽  
Syed Ayesha Fatima ◽  
Jalil Ahmad ◽  
Tausif S. Khan

Coronavirus disease 2019 (COVID-19) is a pandemic disease of modern time with unique and rapid transmission rate and affected almost all the nations without respecting any border. Coronavirus disease 2019 (COVID-19) is arguably the biggest health crisis the world has faced in 21st century. It is an infectious disease and declared pandemic by the World Health Organization. The coronavirus disease 2019 (COVID-19) outbreak, which originated in Wuhan, China, has now spread to 192 countries and administrative regions infecting nearly 800,000 individuals of all ages as of 31 March 2020. Though most infected individuals exhibit mild symptoms including fever, upper respiratory tract symptoms, shortness of breath, and diarrhoea, or are asymptomatic altogether, severe cases of infection can lead to pneumonia, multiple organ failure, and death. Globally, at least 7900 deaths have been directly attributed to COVID19, and this number is expected to rise with the ongoing epidemic. This is particularly crucial as the current outbreak involves a new pathogen (SARS-CoV-2), on which limited knowledge exists of its infectivity and clinical profile. Research is in progress on therapeutic efficacy of various agents including anti-malarials (Chloroquine and Hydroxychloroquine), antiviral drugs, and convalescent serum of recovered patients. Unani system of medicine is one of the traditional systems of medicine which is being explored for providing preventive, supportive and rehabilitative care to patients. Unani system of medicine has a detailed description of drugs that are utilized in many infectious diseases, including respiratory infections. Immune response is essential to eliminate virus and to preclude disease progression to severe stages. Therefore, it is important to summarize the evidence regarding the preventive measures, control options such as immune-stimulator and prophylactic treatment in Unani medicine against Covid19. This review summarizes various pharmacological actions of Unani formulation Tiryaq-e-Arba in Unani literature and various reported pharmacological activities which can possibly provide prevention, control and reduction of complications of this deadly disease.

2020 ◽  

In the past 100 years, the world has faced four distinctly different pandemics: the Spanish flu of 1918-1919, the SARS pandemic of 2003, the H1N1 or “swine flu” pandemic of 2012, and the ongoing COVID-19 pandemic. Each public health crisis exposed specific systemic shortfalls and provided public health lessons for future events. The Spanish flu revealed a nursing shortage and led to a great appreciation of nursing as a profession. SARS showed the importance of having frontline clinicians be able to work with regulators and those producing guidelines. H1N1 raised questions about the nature of a global organization such as the World Health Organization in terms of the benefits and potential disadvantages of leading the fight against a long-term global public health threat. In the era of COVID-19, it seems apparent that we are learning about both the blessing and curse of social media.

Ken Hyland ◽  
Feng (Kevin) Jiang

Abstract Covid-19, the greatest global health crisis for a century, brought a new immediacy and urgency to international bio-medical research. The pandemic generated intense competition to produce a vaccine and contain the virus, creating what the World Health Organization referred to as an ‘infodemic’ of published output. In this frantic atmosphere, researchers were keen to get their research noticed. In this paper, we explore whether this enthusiasm influenced the rhetorical presentation of research and encouraged scientists to “sell” their studies. Examining a corpus of the most highly cited SCI articles on the virus published in the first seven months of 2020, we explore authors’ use of hyperbolic and promotional language to boost aspects of their research. Our results show a significant increase in hype to stress certainty, contribution, novelty and potential, especially regarding research methods, outcomes and primacy. Our study sheds light on scientific persuasion at a time of intense social anxiety.

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