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Web-based university classroom attendance system based on deep learning face recognition

Nowadays, many attendance applications utilise biometric techniques such as the face, fingerprint, and iris recognition. Biometrics has become ubiquitous in many sectors. Due to the advancement of deep learning algorithms, the accuracy rate of biometric techniques has been improved tremendously. Thi...

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Main Authors: Ismail, Nor Azman, Chai, Cheah Wen, Samma, Hussein, Salam, Md. Sah, Hasan, Layla, Abdul Wahab, Nur Haliza, Mohamed, Farhan, Wong, Yee Leng, Rohani, Mohd. Foad
Format: Article
Language:English
Published: Korean Society for Internet Information 2022
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/102738/1/NorAzmanIsmail2022_WebBasedUniversityClassroomAttendance.pdf
http://eprints.utm.my/id/eprint/102738/
http://dx.doi.org/10.3837/tiis.2022.02.008
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spelling my.utm.1027382023-09-20T03:34:30Z http://eprints.utm.my/id/eprint/102738/ Web-based university classroom attendance system based on deep learning face recognition Ismail, Nor Azman Chai, Cheah Wen Samma, Hussein Salam, Md. Sah Hasan, Layla Abdul Wahab, Nur Haliza Mohamed, Farhan Wong, Yee Leng Rohani, Mohd. Foad QA75 Electronic computers. Computer science Nowadays, many attendance applications utilise biometric techniques such as the face, fingerprint, and iris recognition. Biometrics has become ubiquitous in many sectors. Due to the advancement of deep learning algorithms, the accuracy rate of biometric techniques has been improved tremendously. This paper proposes a web-based attendance system that adopts facial recognition using open-source deep learning pre-trained models. Face recognition procedural steps using web technology and database were explained. The methodology used the required pre-trained weight files embedded in the procedure of face recognition. The face recognition method includes two important processes: registration of face datasets and face matching. The extracted feature vectors were implemented and stored in an online database to create a more dynamic face recognition process. Finally, user testing was conducted, whereby users were asked to perform a series of biometric verification. The testing consists of facial scans from the front, right (30 – 45 degrees) and left (30 – 45 degrees). Reported face recognition results showed an accuracy of 92% with a precision of 100% and recall of 90%. Korean Society for Internet Information 2022-02-28 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/102738/1/NorAzmanIsmail2022_WebBasedUniversityClassroomAttendance.pdf Ismail, Nor Azman and Chai, Cheah Wen and Samma, Hussein and Salam, Md. Sah and Hasan, Layla and Abdul Wahab, Nur Haliza and Mohamed, Farhan and Wong, Yee Leng and Rohani, Mohd. Foad (2022) Web-based university classroom attendance system based on deep learning face recognition. KSII Transactions on Internet and Information Systems, 16 (2). pp. 503-523. ISSN 1976-7277 http://dx.doi.org/10.3837/tiis.2022.02.008 DOI:10.3837/tiis.2022.02.008
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Ismail, Nor Azman
Chai, Cheah Wen
Samma, Hussein
Salam, Md. Sah
Hasan, Layla
Abdul Wahab, Nur Haliza
Mohamed, Farhan
Wong, Yee Leng
Rohani, Mohd. Foad
Web-based university classroom attendance system based on deep learning face recognition
description Nowadays, many attendance applications utilise biometric techniques such as the face, fingerprint, and iris recognition. Biometrics has become ubiquitous in many sectors. Due to the advancement of deep learning algorithms, the accuracy rate of biometric techniques has been improved tremendously. This paper proposes a web-based attendance system that adopts facial recognition using open-source deep learning pre-trained models. Face recognition procedural steps using web technology and database were explained. The methodology used the required pre-trained weight files embedded in the procedure of face recognition. The face recognition method includes two important processes: registration of face datasets and face matching. The extracted feature vectors were implemented and stored in an online database to create a more dynamic face recognition process. Finally, user testing was conducted, whereby users were asked to perform a series of biometric verification. The testing consists of facial scans from the front, right (30 – 45 degrees) and left (30 – 45 degrees). Reported face recognition results showed an accuracy of 92% with a precision of 100% and recall of 90%.
format Article
author Ismail, Nor Azman
Chai, Cheah Wen
Samma, Hussein
Salam, Md. Sah
Hasan, Layla
Abdul Wahab, Nur Haliza
Mohamed, Farhan
Wong, Yee Leng
Rohani, Mohd. Foad
author_facet Ismail, Nor Azman
Chai, Cheah Wen
Samma, Hussein
Salam, Md. Sah
Hasan, Layla
Abdul Wahab, Nur Haliza
Mohamed, Farhan
Wong, Yee Leng
Rohani, Mohd. Foad
author_sort Ismail, Nor Azman
title Web-based university classroom attendance system based on deep learning face recognition
title_short Web-based university classroom attendance system based on deep learning face recognition
title_full Web-based university classroom attendance system based on deep learning face recognition
title_fullStr Web-based university classroom attendance system based on deep learning face recognition
title_full_unstemmed Web-based university classroom attendance system based on deep learning face recognition
title_sort web-based university classroom attendance system based on deep learning face recognition
publisher Korean Society for Internet Information
publishDate 2022
url http://eprints.utm.my/id/eprint/102738/1/NorAzmanIsmail2022_WebBasedUniversityClassroomAttendance.pdf
http://eprints.utm.my/id/eprint/102738/
http://dx.doi.org/10.3837/tiis.2022.02.008
_version_ 1778160774221398016
score 13.211869

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