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 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 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.

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 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 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 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 in the International Year of Plant Health

2022 ◽  
Vol 39 (4) ◽  
Author(s):  
José Jorge Gutiérrez-Samperio
Keyword(s):  
Life Forms ◽  
Plant Health ◽  
World Health ◽  
Human Beings ◽  
Pests And Diseases ◽  
The Bible ◽  
United Nations General ◽  
The World ◽  
The Face ◽  
Health Organization

<p>Pests, in their broad sense, have played an important part in the history of humankind. We could say that humans, crops and pests have walked together through life. Codices, glyphs, paintings and countless ancient documents, including the Bible and the Koran, bear witness to this. Humanity has been attacked by its own diseases, but also by those that limit them from obtaining food and deteriorate the environment. COVID-19, which is now troubling us and was declared a pandemic by the World Health Organization in March of 2020, became a part of the list of experiences we have suffered in the past, with pests or epidemics that caused millions of deaths by diseases or famines. It is paradoxical that this health contingency occurs when the United Nations General Assembly, on December 20th, 2018, in its resolution A/RES/73/252 decides to declare 2020 the International Year of Plant Health in order to “highlight the importance of plant health to improve food security, protect the environment and biodiversity and boost economic development” according to the pronouncement by the FAO. For the first time, in an era with great technological and scientific breakthroughs, humanity was aware of its vulnerability against the inevitable evolution of life forms in the face of dilemmas global impact caused by human beings. Thus, the pest or parasite makes its own declaration of existential preeminence through SARS-CoV-2 to remind us that the health of humans or plants is the essence of life and its continuity. But perhaps absolute health is not enough. It is necessary to find a balance in a world overwhelmed by giving so much in return for almost nothing to everyone living on it. If the sensor of our anthropocentric intervention of the world is climate change, then biological chaos is a masterpiece. The reemergence of pests and diseases considered eradicated, or those of zoonotic origin that had never accompanied our existence is a surreal dystopia that we will never be able to deny again.</p>

A novel pyrene based highly selective reversible fluorescent-colorimetric sensor for the rapid detection of Cu2+ ions: application in bio-imaging

2018 ◽  
Vol 10 (9) ◽  
pp. 1063-1073 ◽  
Author(s):  
Anupam Ghorai ◽  
Jahangir Mondal ◽  
Amit Kumar Manna ◽  
Shubhamoy Chowdhury ◽  
Goutam K. Patra
Keyword(s):  
World Health Organization ◽  
Rapid Detection ◽  
Low Cost ◽  
Colorimetric Sensor ◽  
World Health ◽  
Selective Detection ◽  
Imaging Methods ◽  
The World ◽  
Bio Imaging ◽  
Health Organization

A simple, low-cost pyrene derivative containing a benzilmonohydrazone moiety has been exploited for the selective detection of Cu2+ by colorimetric, absorption, emission and bio-imaging methods. The sensitivity of the fluorescence based assay is 7.8 nM for Cu2+, which is far below the limit mentioned in the World Health Organization.

scholarly journals Smart helmet using internet of things

2021 ◽  
Vol 10 (2) ◽  
pp. 90
Author(s):  
Mohamed A. Torad
Keyword(s):  
Emergency Department ◽  
Internet Of Things ◽  
Detection System ◽  
Electrical Signal ◽  
Piezoelectric Sensors ◽  
World Health ◽  
The World ◽  
Health Organization ◽  
Tracking Device ◽  
Accident Detection

The rate of death relative to the size of the world's population has remained constant, according to the world health organization (WHO). WHO targets to minimize the ratio of road death to the half by 2022. This paper discusses a way for accident detection and notification which can decrease this ratio. Piezoelectric sensors used inside a helmet to detect degree of trauma which interpret into electrical signal that used to determine if trauma is serious or not based on predetermined threshold. This trauma can be a result of any type of accidents. So, a detection system established to request immediate help from relatives and emergency department by sending SMS to them contains the longitude and latitude. In normal mode helmet can work as tracking device for the relatives.

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