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 Comparison of Measures adopted to combat COVID 19 Pandemic by different countries in WHO regions

2020 ◽  
Vol 32 (2 (Supp)) ◽  
pp. 288-299
Author(s):  
Shubha DB ◽  
Malathesh Undi ◽  
Rachana Annadani ◽  
Ayesha Siddique
Keyword(s):  
Virus Disease ◽  
Management Strategies ◽  
Vital Role ◽  
Control Measures ◽  
World Health ◽  
Contact Tracing ◽  
Limited Information ◽  
The World ◽  
The Difference ◽  
Health Organization

Since the emergence of Corona Virus Disease 19 (COVID 19) in China in December 2019, a lot of significant decisions have been taken by the World Health Organization (WHO) and several countries across the globe. As the world reels under the threat of rapid increase in the number of cases and is planning strategies with the limited information available on the virus, it is essential to learn from the experience of countries across the globe. Hence, we selected a few countries in five WHO regions based on their COVID 19 caseload, management strategies and outcome and compared some of the important measures taken by them to contain the spread of infection. Strategies like extensive testing and contact tracing, strict quarantine and isolation measures, Hospital preparedness, complete restriction of non-essential travel, strict border control measures and social distancing measures play a vital role in containment of the spread. All the countries faced the novel strain of virus and implemented similar strategies as per the guidance of WHO, but the extent of preparedness, swiftness with which the decisions were made and the scale of measures made the difference.

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.

scholarly journals COVID-19 in the Eyes of Community Leaders in Selected Rural Districts in Eastern Uganda

2021 ◽  
pp. 20-37
Author(s):  
Abdul Walusansa ◽  
Jacob S. Iramiot ◽  
Joseph L. Mpagi ◽  
Ali Kudamba ◽  
Shaban A. Okurut ◽  
...  
Keyword(s):  
Virus Disease ◽  
Control Measures ◽  
World Health ◽  
Community Leaders ◽  
Effective Control ◽  
Convenience Sample ◽  
Rural Settings ◽  
The World ◽  
Knowledge Levels ◽  
Eastern Uganda

Introduction: Corona virus disease (COVID-19) is one of the topmost global hindrances to human existence. Rural settings have been reported to be more vulnerable in some parts of the world. In Uganda, community leaders in rural villages are among the immediate personnel mandated to support compliance with preventive guidelines, and to identify and report/deliver COVID-19 cases to health units. We examined the potential risks of COVID-19 transmission, knowledge levels, perceptions and opinions of Village Council Leaders (VCLs) in selected districts in Eastern Uganda, to support the design of risk-based COVID-19 control measures in rural settings, to protect lives better. Methodology: A convenience sample of ten VLCs were purposively selected in three districts in Eastern Uganda. Pretested questionnaires and in-depth interviews were used to assess the knowledge levels, perceptions, and opinions of respondents about COVID-19. An observational survey was also conducted to examine the barriers to effective control of COVID-19, with reference to health guidelines set by the Ugandan government and the World Health Organization. Data was analyzed using HyperRESEARCH 2.8 software, and STATA version-15.0. Results: Eighty percent of VCLs reported that they were formally engaged in the fight against COVID-19, and the common means of engagement were; dissemination of COVID-19 information by word of mouth, regulation of public events, and monitoring of visitors that come from distant places. All clients reported having received some information about this pandemic, but there was generally low knowledge on some vital aspects: 70% of the respondents did not know the meaning of COVID-19; 100% were uninformed on these common symptoms; headache, sore throat, nausea, and loss of taste & smell; 10% did not know if willingness to conform to health guidelines may affect COVID-19 prevention, and they believed that domestic animals are viable vectors. Radio was the commonest source of COVID-19 information, but it was confounded by poor quality of radio-signals. Most respondents were of the view that in the communities they lead; inaccessibility to authentic health information, financial constraints, and belief that COVID-19 is a fallacy, were some of the leading obstacles to the fight against the pandemic. Low awareness and misconceptions on COVID-19 could be explained by; technological challenges, low literacy levels, and dissemination of wrong information about this pandemic. From the observational survey, the major factors which might intensify the risk of COVID-19 spread were: scarcity of requirements for hand hygiene, face protection, violation of health guidelines and directives, porous borders, terrain, and use of potentially polluted open water sources. Conclusion: Communities in Eastern Uganda are vulnerable to the drastic spread of COVID-19 due to challenges related to: low awareness, poor compliance with preventive guidelines, finances, technology, terrain, illiteracy, scarcity of protective wear and hygiene resources. Awareness creation, material aid, execution of preventive rules, and more research on COVID-19 are warranted.

scholarly journals Covhindia: Deep Learning Framework for Sentiment Polarity Detection of Covid-19 Tweets in Hindi

2020 ◽  
Vol 9 (5) ◽  
pp. 23-34
Author(s):  
Purva Singh
Keyword(s):  
Deep Learning ◽  
Machine Translation ◽  
Virus Disease ◽  
Performance Comparison ◽  
Sentiment Classification ◽  
World Health ◽  
Detection Accuracy ◽  
Learning Framework ◽  
The World ◽  
Hindi Language

On 11th March 2020, the World Health Organization (WHO) declared Corona Virus Disease of 2019 (COVID-19) as a pandemic. Over time, the exponential growth of this disease has highlighted a mixture of sentiments expressed by the general population from various parts of the world speaking varied languages. It is, therefore, essential to analyze the public sentiment during this wave of the pandemic. While much work prevails to determine the sentiment polarity for tweets related to COVID-19, expressed in the English language, we still need to work on public sentiments expressed in languages other than English. This paper proposes a framework, Covhindia, a deep-learning framework that performs sentiment polarity detection of tweets related to COVID-19 posted in the Hindi language on the Twitter platform. The proposed framework leverages machine translation on Hindi tweets and passes the translated data as input to a deep learning model which is trained on an English corpus of COVID-19 tweets posted from India [18]. The paper compares nine deep learning models' performances in classifying the sentiment polarity on an English dataset. Performance comparison of these architectures reveals that the BERT model had the best polarity detection accuracy on the English corpus. As part of testing the Covhindia’s accuracy in performing sentiment classification on Hindi tweets, the paper employs a separate dataset developed using a python library called Tweepy to extract Hindi tweets related to COVID-19. Experimental results reveal that Covhindia achieved state-of-the-art accuracy in classifying COVID-19 tweets posted in the Hindi language. The use of open-source machine translation tools paved the way for leveraging Covhindia for performing multilingual sentiment classification on COVID-19 tweets. For the benefit of the research community, the code and Jupyter Notebooks related to this paper are available on Github

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 The Temperature Screening and Face Mask Detection for Preventing Spread of COVID-19

2021 ◽  
pp. 639-646
Author(s):  
Gautami Kale ◽  
Akash Jasoriya ◽  
Divesh Jain ◽  
Abhilasha Narote
Keyword(s):  
Public Service ◽  
Social Distance ◽  
Service Providers ◽  
Virus Disease ◽  
National Level ◽  
Face Mask ◽  
Protection Method ◽  
The World ◽  
Public Areas ◽  
Screen Temperature

Corona virus disease 2019 has affected the world seriously. One major protection method for people is to wear masks in public areas. Furthermore, many public service providers require customers to use the service only if they wear masks correctly. On national level, temperature screening by employers is not mandatory. However, it is strongly recommended for businesses with more than 50 employees and businesses where maintaining social distance may not be realistic. Also government decided to reopen all religious places in this case temperature screening and mask plays crucial role hence we proposed system which automatically detects mask and screen temperature and allows only those who are wearing mask and has body temperature within range. Here we used infrared thermometer for thermal scanning and CNN algorithm for mask detection.

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 Deep Learning Applications and Perspectives COVID 19

2020 ◽  
pp. 487-490
Author(s):  
Suresh P ◽  
Robert P ◽  
Padmavathi A
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Control Measures ◽  
World Health ◽  
Technological Support ◽  
Deep Learning Algorithm ◽  
Corona Virus ◽  
The World ◽  
The Government ◽  
Health Organization

An anticipated outbreak of corona virus (COVID-19) brings out the severe acute respiratory syndrome and corona-virus-2(SARS-COV-2) attacked in Wuhan city, china beginning of February 2020. World health organization (WHO) announced the outburst of pandemic disease through public health emergency of international concern. A variety of control measures has been taken by the government to control the disease. The investigation can be done about the pathogen and current epidemic. The world healthcare system has a major concern for new infectious diseases like covid-19 and needs new technological support. Deep learning in Artificial intelligence (AI) helps the world by safeguard the people from pandemic disease. Our aim is to investigate the AI based deep learning algorithm to analyze, prevent and prepare to defend against covid-19 and similar infectious disease.

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