scholarly journals COVIDSAVIOR: A Novel Sensor-Fusion and Deep Learning Based Framework for Virus Outbreaks

2021 ◽  
Vol 9 ◽  
Author(s):  
Sharnil Pandya ◽  
Anirban Sur ◽  
Nitin Solke
Keyword(s):  
Deep Learning ◽  
Body Temperature ◽  
Real Time ◽  
Sensor Fusion ◽  
Face Mask ◽  
Temperature Threshold ◽  
Mask Condition ◽  
Essential Information ◽  
The Individual ◽  
Scanning Mechanism

The presented deep learning and sensor-fusion based assistive technology (Smart Facemask and Thermal scanning kiosk) will protect the individual using auto face-mask detection and auto thermal scanning to detect the current body temperature. Furthermore, the presented system also facilitates a variety of notifications, such as an alarm, if an individual is not wearing a mask and detects thermal temperature beyond the standard body temperature threshold, such as 98.6°F (37°C). Design/methodology/approach—The presented deep Learning and sensor-fusion-based approach can also detect an individual in with or without mask situations and provide appropriate notification to the security personnel by raising the alarm. Moreover, the smart tunnel is also equipped with a thermal sensing unit embedded with a camera, which can detect the real-time body temperature of an individual concerning the prescribed body temperature limits as prescribed by WHO reports. Findings—The investigation results validate the performance evaluation of the presented smart face-mask and thermal scanning mechanism. The presented system can also detect an outsider entering the building with or without mask condition and be aware of the security control room by raising appropriate alarms. Furthermore, the presented smart epidemic tunnel is embedded with an intelligent algorithm that can perform real-time thermal scanning of an individual and store essential information in a cloud platform, such as Google firebase. Thus, the proposed system favors society by saving time and helps in lowering the spread of coronavirus.

scholarly journals Real Time Face Mask Identification Using Deep Learning

2021 ◽  
Vol 1916 (1) ◽  
pp. 012077
Author(s):  
M Sujaritha ◽  
S Kabilan ◽  
M Manikandan ◽  
S Nanda Kisore
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Face Mask

scholarly journals Use of Artificial Intelligence for Face Detection with Face Mask in Real Time to Control the Entrance to an Entity

2021 ◽  
Vol 11 (11) ◽  
pp. 68-75
Author(s):  
Enrique Lee Huamaní ◽  
◽  
Lilian Ocares Cunyarachi
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Face Detection ◽  
Face Mask ◽  
Economic Activities ◽  
Facial Mask ◽  
The Face ◽  
The Individual ◽  
A Company ◽  
Selection Of

Due to the pandemic caused by Covid-19, daily life has changed significantly. For this reason, biosecurity measures have been implemented to prevent the spread of the virus as an effective way to reactivate economic activities. In this sense, the present paper focuses on real-time face detection as a measure of control at the entrance to an entity, thus avoiding the spread of the virus while recognizing the identity of workers despite the use of masks and thus reducing the risk of entry of individuals outside the organization. Therefore, the objective is to contribute to the security of a company through the application of machine learning methodology. The selection of methodology is justified due to the adaptation of the same according to the interests of this project. Consequently, algorithms were used in a progressive manner, obtaining as a result the control system that was intended, since each particularity of the face of the individual was recognized in relation to its corresponding identification. Finally, the results of this article benefit the security of organizations regardless of their field or sector. Keywords— Control, Detection, Facial Recognition, Facial Mask, Face recognition, Machine learning.

MaskNet: CNN for Real-Time Face Mask Detection Based on Deep Learning Techniques

2021 ◽  
pp. 87-97
Author(s):  
Ismail Nasri ◽  
Mohammed Karrouchi ◽  
Hajar Snoussi ◽  
Abdelhafid Messaoudi ◽  
Kamal Kassmi
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Face Mask ◽  
Learning Techniques

scholarly journals Detection of Hidden Facial Surface Masking in Stored and Real Time Captured Images: A Deep Learning Perspective in Covid Time

2021 ◽  
Vol 38 (6) ◽  
pp. 1875-1885
Author(s):  
Ruchi Jayaswal ◽  
Manish Dixit
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Preventive Measure ◽  
Face Mask ◽  
Histogram Equalization ◽  
Test Accuracy ◽  
The Face ◽  
Novel Coronavirus ◽  
Face Detector

A novel coronavirus has spread over the world and has become an outbreak. This, according to a WHO report, is an infectious disease that aims to spread. As a consequence, taking precautions is the only method to avoid catching this virus. The most important preventive measure against COVID-19 is to wear a mask. In this paper, a framework is designed for face mask detection using a deep learning approach. This paper aims to predict a person having a mask or unmask and also presents a proposed dataset named RTFMD (Real-Time Face Mask Dataset) to accomplish this objective. We have also taken the RFMD dataset from the internet to analyze the performance of system. Contrast Limited Adaptive Histogram Equalization (CLAHE) technique is applied at the time of pre-processing to enhance the visual quality of images. Subsequently, Inceptionv3 model used to train the face mask images and SSD face detector model has been used for face detection. Therefore, this paper proposed a model CLAHE-SSD_IV3 to classify the mask or without mask images. The system is also tested at VGG16, VGG19, Xception, MobilenetV2 models at different hyperparameters values and analyze them. Furthermore, compared the result of the proposed dataset RTFMD with the RFMD dataset. Additionally, proposed approach is compared with the existing approach on Face Mask dataset and RTFMD dataset. The outcomes have obtained 98% test accuracy on this proposed dataset RTFMD while 97% accuracy on the RFMD dataset in real-time.

scholarly journals Deep Learning Model Based on Mobile-Net with Haar-like Algorithm for Masked Face Recognition at Nuclear Facilities

2021 ◽  
Vol 10 (7) ◽  
pp. 18-23
Author(s):  
Nadia.M. Nawwar* ◽  
Kasban . ◽  
Salama May
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Face Mask ◽  
Learning Model ◽  
Nuclear Facility ◽  
Nuclear Facilities ◽  
Novel Technique ◽  
The World ◽  
The Individual ◽  
Deep Learning Model

During the spread of the COVID-I9 pandemic in early 2020, the WHO organization advised all people in the world to wear face-mask to limit the spread of COVID-19. Many facilities required that their employees wear face-mask. For the safety of the facility, it was mandatory to recognize the identity of the individual wearing the mask. Hence, face recognition of the masked individuals was required. In this research, a novel technique is proposed based on a mobile-net and Haar-like algorithm for detecting and recognizing the masked face. Firstly, recognize the authorized person that enters the nuclear facility in case of wearing the masked-face using mobile-net. Secondly, applying Haar-like features to detect the retina of the person to extract the boundary box around the retina compares this with the dataset of the person without the mask for recognition. The results of the proposed modal, which was tested on a dataset from Kaggle, yielded 0.99 accuracies, a loss of 0.08, F1.score 0.98.

scholarly journals Application of Deep-Learning Methods to Real Time Face Mask Detection

2021 ◽  
Vol 19 (6) ◽  
pp. 994-1001
Author(s):  
Diego Gonzalez Dondo ◽  
Javier Andres Redolfi ◽  
R. Gaston Araguas ◽  
Daiana Garcia
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Face Mask ◽  
Learning Methods

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.

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