Real Time Face Mask Identification Using Deep Learning

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.

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

Digital Technologies and Applications - Lecture Notes in Networks and Systems ◽

10.1007/978-3-030-73882-2_9 ◽

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

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Real-Time Deep Learning Face Mask Detection Model During COVID-19

Communication and Intelligent Systems - Lecture Notes in Networks and Systems ◽

10.1007/978-981-16-1089-9_11 ◽

2021 ◽

pp. 121-131

Author(s):

Amit Juyal ◽

Aditya Joshi

Keyword(s):

Deep Learning ◽

Real Time ◽

Face Mask ◽

Detection Model

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

Traitement du signal ◽

10.18280/ts.380632 ◽

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.

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COVID-19 pandemic deep learning implementations of prediction of disease with data analysis and real-time face-mask detection with camera

10.1109/aimv53313.2021.9670960 ◽

2021 ◽

Author(s):

Amrut Khatavkar ◽

Namit Kharade ◽

Geeta Navale ◽

Tanaji Khadtare

Keyword(s):

Deep Learning ◽

Data Analysis ◽

Real Time ◽

Face Mask

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

IEEE Latin America Transactions ◽

10.1109/tla.2021.9451245 ◽

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

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Real-Time Face Mask Detection using Deep Learning

Journal of Technology Management for Growing Economies ◽

10.15415/jtmge.2021.121003 ◽

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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