Face recognition employees attendance system

Face recognition uses a variety of technologies and locations to carry out the attendance system. In order to recognise a face in real-time settings utilising a specific purpose device, attendance systems require accurate results. Video architecture is also achieved in our design by piercing the cam...

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Main Author:	Abdullah Al Nasser, Munef Hasan
Format:	Thesis
Language:	English English English
Published:	2022
Subjects:	TA1501-1820 Applied optics Photonics
Online Access:	http://eprints.uthm.edu.my/6983/1/24p%20MUNEF%20HASAN%20ABDULLAH%20AL%20NASSER.pdf http://eprints.uthm.edu.my/6983/2/MUNEF%20HASAN%20ABDULLAH%20AL%20NASSER%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/6983/3/MUNEF%20HASAN%20ABDULLAH%20AL%20NASSER%20WATERMARK.pdf
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id	my-uthm-ep.6983
record_format	uketd_dc
spelling	my-uthm-ep.69832022-04-26T06:26:19Z Face recognition employees attendance system 2022-02 Abdullah Al Nasser, Munef Hasan TA1501-1820 Applied optics. Photonics Face recognition uses a variety of technologies and locations to carry out the attendance system. In order to recognise a face in real-time settings utilising a specific purpose device, attendance systems require accurate results. Video architecture is also achieved in our design by piercing the camera via a stoner- friendly interface. The Overeater (Histogram of Acquainted Grade) algorithm is used to recognise and segment the face from the VHS frame. Garbling a photo using the Overeater method to obtain a simplified interpretation of the image is the first phase, or pre-processing stage. Find the part of the image that most closely resembles a general Overeater encoding of a face using this simplified image. Also in the next step, figuring out the face's disguise by chancing the primary landmarks in the face. Once we've located those landmarks, we can utilise them to anchor the image such that the eyes and mouth are centred. Run the centred face image through a neural network that understands how to measure facial traits. Save those 128 measurements for later. Examine all of the faces we've measured in the past to find who has the most similar measurements to ours. That's the result of our match. Overall, we developed a Python programme that takes an image from a database and does all of the necessary changes for recognition, as well as checks the image in videos or in real time by accessing the camera using a Stoner-friendly interface. After a successful match is made, the name and time of the individual in attendance is recorded. 2022-02 Thesis http://eprints.uthm.edu.my/6983/ http://eprints.uthm.edu.my/6983/1/24p%20MUNEF%20HASAN%20ABDULLAH%20AL%20NASSER.pdf text en public http://eprints.uthm.edu.my/6983/2/MUNEF%20HASAN%20ABDULLAH%20AL%20NASSER%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/6983/3/MUNEF%20HASAN%20ABDULLAH%20AL%20NASSER%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	TA1501-1820 Applied optics Photonics
spellingShingle	TA1501-1820 Applied optics Photonics Abdullah Al Nasser, Munef Hasan Face recognition employees attendance system
description	Face recognition uses a variety of technologies and locations to carry out the attendance system. In order to recognise a face in real-time settings utilising a specific purpose device, attendance systems require accurate results. Video architecture is also achieved in our design by piercing the camera via a stoner- friendly interface. The Overeater (Histogram of Acquainted Grade) algorithm is used to recognise and segment the face from the VHS frame. Garbling a photo using the Overeater method to obtain a simplified interpretation of the image is the first phase, or pre-processing stage. Find the part of the image that most closely resembles a general Overeater encoding of a face using this simplified image. Also in the next step, figuring out the face's disguise by chancing the primary landmarks in the face. Once we've located those landmarks, we can utilise them to anchor the image such that the eyes and mouth are centred. Run the centred face image through a neural network that understands how to measure facial traits. Save those 128 measurements for later. Examine all of the faces we've measured in the past to find who has the most similar measurements to ours. That's the result of our match. Overall, we developed a Python programme that takes an image from a database and does all of the necessary changes for recognition, as well as checks the image in videos or in real time by accessing the camera using a Stoner-friendly interface. After a successful match is made, the name and time of the individual in attendance is recorded.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Abdullah Al Nasser, Munef Hasan
author_facet	Abdullah Al Nasser, Munef Hasan
author_sort	Abdullah Al Nasser, Munef Hasan
title	Face recognition employees attendance system
title_short	Face recognition employees attendance system
title_full	Face recognition employees attendance system
title_fullStr	Face recognition employees attendance system
title_full_unstemmed	Face recognition employees attendance system
title_sort	face recognition employees attendance system
granting_institution	Universiti Tun Hussein Malaysia
granting_department	Fakulti Kejuruteraan Elektrik dan Elektronik
publishDate	2022
url	http://eprints.uthm.edu.my/6983/1/24p%20MUNEF%20HASAN%20ABDULLAH%20AL%20NASSER.pdf http://eprints.uthm.edu.my/6983/2/MUNEF%20HASAN%20ABDULLAH%20AL%20NASSER%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/6983/3/MUNEF%20HASAN%20ABDULLAH%20AL%20NASSER%20WATERMARK.pdf
_version_	1747831103105794048

Face recognition employees attendance system

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