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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
http://eprints.uthm.edu.my/6962/
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spelling my.uthm.eprints.69622022-04-18T01:53:15Z http://eprints.uthm.edu.my/6962/ Masked face detection system using google firebase 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 NonPeerReviewed text en http://eprints.uthm.edu.my/6962/1/24p%20ANIS%20ZAHIRAH%20AZMAN.pdf text en http://eprints.uthm.edu.my/6962/2/ANIS%20ZAHIRAH%20AZMAN%20COPYRIGHT%20DECLARATION.pdf text en http://eprints.uthm.edu.my/6962/3/ANIS%20ZAHIRAH%20AZMAN%20WATERMARK.pdf Azman, Anis Zahirah (2021) Masked face detection system using google firebase. Masters thesis, Universiti Tun Hussein Malaysia.
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
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
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
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
http://eprints.uthm.edu.my/6962/
_version_ 1738581557828911104
score 13.211869

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