Masked face detection system using google firebase

The global health epidemic caused by the breakout of a coronavirus disease in 2019 (COVID-19) has had a significant impact on way we view our environment and live our daily lives. The number of people infected with Covid-19 is rapidly increasing. As a result, several countries are facing economic di...

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Main Author:	Azman, Anis Zahirah
Format:	Thesis
Language:	English English English
Published:	2021
Subjects:	QA76 Computer software
Online Access:	http://eprints.uthm.edu.my/6962/1/24p%20ANIS%20ZAHIRAH%20AZMAN.pdf http://eprints.uthm.edu.my/6962/2/ANIS%20ZAHIRAH%20AZMAN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/6962/3/ANIS%20ZAHIRAH%20AZMAN%20WATERMARK.pdf
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id	my-uthm-ep.6962
record_format	uketd_dc
spelling	my-uthm-ep.69622022-04-18T01:53:15Z Masked face detection system using google firebase 2021-10 Azman, Anis Zahirah QA76 Computer software The global health epidemic caused by the breakout of a coronavirus disease in 2019 (COVID-19) has had a significant impact on way we view our environment and live our daily lives. The number of people infected with Covid-19 is rapidly increasing. As a result, several countries are facing economic disasters, recession, and other problems. Separating ourselves from society, remaining at home, and detaching ourselves from the outside world is one thing we should do. But that is no longer an option; people must work to exist, and no one can live in their homes eternally. People should wear masks and maintain social distance as a precaution. As a result, detecting face masks has become a critical responsibility in assisting the global community. This report describes a simplified method for accomplishing this goal utilizing TensorFlow, Keras, OpenCV, and Convolutional Neural Networks, as well as some basic Machine Learning packages. The suggested approach successfully recognizes the face in a picture and then determines whether it is covered by a mask. If a person is discovered without a face mask, an alert warning is issued, and the person's face is captured. In addition, the value of masking and unmasking faces is saved in the cloud for future use. By using this deep learning, enable the system to be faster and more precise to detect the faces and as a result, the accuracy of mask and unmask faces detection is higher than 90%. As all the facilities open and the number of COVID-19 cases continues to rise across the country, everyone must adhere to the safety precautions until the outbreak is over. As a result, this module assist in recognizing people wearing masks when entering premises. 2021-10 Thesis http://eprints.uthm.edu.my/6962/ http://eprints.uthm.edu.my/6962/1/24p%20ANIS%20ZAHIRAH%20AZMAN.pdf text en public http://eprints.uthm.edu.my/6962/2/ANIS%20ZAHIRAH%20AZMAN%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/6962/3/ANIS%20ZAHIRAH%20AZMAN%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Malaysia Fakulti Kejuruteraan Elektrik dan Elektronik
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Azman, Anis Zahirah Masked face detection system using google firebase
description	The global health epidemic caused by the breakout of a coronavirus disease in 2019 (COVID-19) has had a significant impact on way we view our environment and live our daily lives. The number of people infected with Covid-19 is rapidly increasing. As a result, several countries are facing economic disasters, recession, and other problems. Separating ourselves from society, remaining at home, and detaching ourselves from the outside world is one thing we should do. But that is no longer an option; people must work to exist, and no one can live in their homes eternally. People should wear masks and maintain social distance as a precaution. As a result, detecting face masks has become a critical responsibility in assisting the global community. This report describes a simplified method for accomplishing this goal utilizing TensorFlow, Keras, OpenCV, and Convolutional Neural Networks, as well as some basic Machine Learning packages. The suggested approach successfully recognizes the face in a picture and then determines whether it is covered by a mask. If a person is discovered without a face mask, an alert warning is issued, and the person's face is captured. In addition, the value of masking and unmasking faces is saved in the cloud for future use. By using this deep learning, enable the system to be faster and more precise to detect the faces and as a result, the accuracy of mask and unmask faces detection is higher than 90%. As all the facilities open and the number of COVID-19 cases continues to rise across the country, everyone must adhere to the safety precautions until the outbreak is over. As a result, this module assist in recognizing people wearing masks when entering premises.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Azman, Anis Zahirah
author_facet	Azman, Anis Zahirah
author_sort	Azman, Anis Zahirah
title	Masked face detection system using google firebase
title_short	Masked face detection system using google firebase
title_full	Masked face detection system using google firebase
title_fullStr	Masked face detection system using google firebase
title_full_unstemmed	Masked face detection system using google firebase
title_sort	masked face detection system using google firebase
granting_institution	Universiti Tun Hussein Malaysia
granting_department	Fakulti Kejuruteraan Elektrik dan Elektronik
publishDate	2021
url	http://eprints.uthm.edu.my/6962/1/24p%20ANIS%20ZAHIRAH%20AZMAN.pdf http://eprints.uthm.edu.my/6962/2/ANIS%20ZAHIRAH%20AZMAN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/6962/3/ANIS%20ZAHIRAH%20AZMAN%20WATERMARK.pdf
_version_	1747831099685339136

Masked face detection system using google firebase

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