scholarly journals Real Time Face Mask Detection and Thermal Screening with Audio Response for COVID-19

2021 ◽  
Vol 11 (4) ◽  
pp. 2703-2714
Author(s):  
M. Sivasankara Rao ◽  
K. Tejasree ◽  
P. Sathwik ◽  
P. Sandeep Kumar ◽  
M. Sailohith
Keyword(s):  
Body Temperature ◽  
Face Mask ◽  
Vital Role ◽  
World Health ◽  
Multiple Sources ◽  
Health Crisis ◽  
The Earth ◽  
The Face ◽  
Health Organization ◽  
Deep Learning Model

The coronavirus COVID-19 pandemic is continuously spreading until now everywhere on the earth, and causing a severe health crisis. So the helpful and safe-keeping method is wearing a face mask in all areas where people are gathered, according to the World Health Organization (WHO). Along with the face mask, body temperature and sanitization also plays a vital role in being safer. Thus, monitoring the individuals that are wearing the mask or not is more significant. In this paper, we propose a system that uses TensorFlow, Keras, MobileNetV2, and OpenCV to detect the face mask. A dataset contains images of persons with and without masks obtained from multiple sources and trained on a deep learning model. Then the automatic temperature checking and Sanitation are done. Finally, the proposed system gives an audio/voice output whether the face mask is present or not, the person's body temperature. Our approach would be beneficial in reducing the spread of this infectious disease and will encourage people to use face masks, getting regularly sanitized and monitoring the temperature can keep the workplace safe.

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 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 Facial Recognition System for People with and without Face Mask in Times of the COVID-19 Pandemic

2021 ◽  
Vol 13 (12) ◽  
pp. 6900
Author(s):  
Jonathan S. Talahua ◽  
Jorge Buele ◽  
P. Calvopiña ◽  
José Varela-Aldás
Keyword(s):  
Facial Recognition ◽  
Face Mask ◽  
Recognition System ◽  
Classification Model ◽  
World Health ◽  
The Face ◽  
Recognition Systems ◽  
Facial Recognition System ◽  
Health Organization ◽  
Face Detector

In the face of the COVID-19 pandemic, the World Health Organization (WHO) declared the use of a face mask as a mandatory biosafety measure. This has caused problems in current facial recognition systems, motivating the development of this research. This manuscript describes the development of a system for recognizing people, even when they are using a face mask, from photographs. A classification model based on the MobileNetV2 architecture and the OpenCv’s face detector is used. Thus, using these stages, it can be identified where the face is and it can be determined whether or not it is wearing a face mask. The FaceNet model is used as a feature extractor and a feedforward multilayer perceptron to perform facial recognition. For training the facial recognition models, a set of observations made up of 13,359 images is generated; 52.9% images with a face mask and 47.1% images without a face mask. The experimental results show that there is an accuracy of 99.65% in determining whether a person is wearing a mask or not. An accuracy of 99.52% is achieved in the facial recognition of 10 people with masks, while for facial recognition without masks, an accuracy of 99.96% is obtained.

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 Reliability of real-time RT-PCR tests to detect SARS-Cov-2: A literature review

2020 ◽  
Vol 11 ◽  
pp. 13 ◽  
Author(s):  
Clément Bezier ◽  
Géraldine Anthoine ◽  
Abdérafi Charki
Keyword(s):  
Real Time ◽  
World Health ◽  
Detection Methods ◽  
Rt Pcr ◽  
Health Crisis ◽  
The Face ◽  
Commercial Kits ◽  
Health Organization ◽  
Technical Capacity ◽  
The Impact

In the face of the COVID-19 (Coronavirus Disease 2019) pandemic, the World Health Organization (WHO) has urged countries to test the population more widely. Clinical laboratories have been confronted with a huge demand for testing and have had to make urgent preparations for staff training, to establish new analytical processes, reorganize the workspace, and stock up on specific equipment and diagnostic test kits. The reliability of SARS-Cov-2 test results is of critical importance, given the impact it has on patient care and the management of the health crisis. A review of the literature available for the period leading up to and including June 2020 on the reliability of SARS-Cov-2 (Severe Acute Respiratory Syndrome Coronavirus) detection methods using real-time RT PCR (Reverse Transcription - Polymerase Chain Reaction) brings together the primary factors teams of scientists claim or demonstrate to affect the reliability of results. A description is given of the RT-PCR testing method, followed by a presentation of the characteristics and validation techniques used. A summary of data from the literature on the reliability of tests and commercial kits for SARS-Cov-2 detection, including current uncertainties with regard to the molecular targets selected and genetic diversity of SARS-Cov-2 is provided. The limitations and perspectives are then discussed in detail in the light of the bibliographic data available. Many questions have been asked that still remain unanswered. The lack of knowledge about this novel virus, which appeared at the end of 2019, has a significant impact on the technical capacity to develop reliable, rapid and practical tools for its detection.

scholarly journals Gestión comunicativa de crisis de las oficinas nacionales de turismo de España e Italia ante la Covid-19

2020 ◽  
Author(s):  
Assumpció Huertas ◽  
Andrea Oliveira ◽  
Michele Girotto
Keyword(s):  
Crisis Communication ◽  
Communication Strategies ◽  
World Health ◽  
Crisis Communications ◽  
Health Crisis ◽  
The World ◽  
The Face ◽  
Health Organization ◽  
Medios Sociales ◽  
Official Declaration

This study analyzes how the national tourism organizations (NTOs) of Spain and Italy managed their crisis communication to deal with Covid-19. The study examines the messages published by the Twitter accounts of the NTOs from the beginning or the detection of the first patient until one month after the official declaration of the pandemic by the World Health Organization. The results reveal two different ways of managing crisis communications in the face of Covid-19, both in the treatment of the topics and in the timing of publication, at the same time generating different reactions and engagement among users. The Agenzia Nazionale Italiana del Turismo was faster and more active in the first moments of the health crisis compared with the Oficina de Turismo de España. This study can contribute to the development of communication strategies on social media by NTOs during different periods of such crises as well as communication actions to enhance the touristic image of their destinations. Resumen La crisis sanitaria de la Covid-19 está afectando a diversos sectores económicos, especialmente al turístico. Esta investigación persigue conocer cómo las oficinas nacionales de turismo (ONTs) de España e Italia gestionaron su comunicación de crisis ante la Covid-19. El estudio se llevó a cabo mediante el análisis de contenido de los mensajes publicados en las cuentas de Twitter de las ONTs desde el inicio o la detección del primer paciente hasta un mes después de la declaración oficial de pandemia por la Organización Mundial de la Salud. Los resultados revelan dos maneras distintas de administrar la comunicación de crisis ante la Covid-19 tanto en el tratamiento de los temas como en los tiempos de la publicación, que a la vez generan distintas reacciones y engagement entre los usuarios, siendo la Agenzia Nazionale Italiana del Turismo más rápida y activa en el primer momento de la crisis en comparación con la Oficina de Turismo de España. Este estudio es una contribución para las ONTs de los países que sufren crisis sanitarias en la creación de sus estrategias comunicativas en los medios sociales, tanto en los diferentes períodos de la crisis como respecto a las acciones comunicativas para recuperar la imagen turística de los destinos.

Gestión comunicativa de crisis de las oficinas nacionales de turismo de España e Italia ante la Covid-19

2020 ◽  
Author(s):  
Assumpció Huertas ◽  
Andrea Oliveira ◽  
Michele Girotto
Keyword(s):  
Crisis Communication ◽  
Communication Strategies ◽  
World Health ◽  
Crisis Communications ◽  
Health Crisis ◽  
The World ◽  
The Face ◽  
Health Organization ◽  
Medios Sociales ◽  
Official Declaration

This study analyzes how the national tourism organizations (NTOs) of Spain and Italy managed their crisis communication to deal with Covid-19. The study examines the messages published by the Twitter accounts of the NTOs from the beginning or the detection of the first patient until one month after the official declaration of the pandemic by the World Health Organization. The results reveal two different ways of managing crisis communications in the face of Covid-19, both in the treatment of the topics and in the timing of publication, at the same time generating different reactions and engagement among users. The Agenzia Nazionale Italiana del Turismo was faster and more active in the first moments of the health crisis compared with the Oficina de Turismo de España. This study can contribute to the development of communication strategies on social media by NTOs during different periods of such crises as well as communication actions to enhance the touristic image of their destinations. Resumen La crisis sanitaria de la Covid-19 está afectando a diversos sectores económicos, especialmente al turístico. Esta investigación persigue conocer cómo las oficinas nacionales de turismo (ONTs) de España e Italia gestionaron su comunicación de crisis ante la Covid-19. El estudio se llevó a cabo mediante el análisis de contenido de los mensajes publicados en las cuentas de Twitter de las ONTs desde el inicio o la detección del primer paciente hasta un mes después de la declaración oficial de pandemia por la Organización Mundial de la Salud. Los resultados revelan dos maneras distintas de administrar la comunicación de crisis ante la Covid-19 tanto en el tratamiento de los temas como en los tiempos de la publicación, que a la vez generan distintas reacciones y engagement entre los usuarios, siendo la Agenzia Nazionale Italiana del Turismo más rápida y activa en el primer momento de la crisis en comparación con la Oficina de Turismo de España. Este estudio es una contribución para las ONTs de los países que sufren crisis sanitarias en la creación de sus estrategias comunicativas en los medios sociales, tanto en los diferentes períodos de la crisis como respecto a las acciones comunicativas para recuperar la imagen turística de los destinos.

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%.

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