Real-time mask detection and face recognition using eigenfaces and local binary pattern histogram for attendance system
Face recognition is gaining popularity as one of the biometrics methods for an attendance system in an organization. Due to the pandemic, the common face recognition system needs to be modified to meet the current needs, whereby facemask detection is necessary. The main objective of this paper is to...
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Institute of Advanced Engineering and Science (IAES)
2021
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Online Access: | https://eprints.ums.edu.my/id/eprint/30051/2/Real-time%20mask%20detection%20and%20face%20recognition%20using%20eigenfaces%20and%20local%20binary%20pattern%20histogram%20for%20attendance%20system-Abstract.pdf https://eprints.ums.edu.my/id/eprint/30051/1/Real-time%20mask%20detection%20and%20face%20recognition%20using%20eigenfaces%20and%20local%20binary%20pattern%20histogram%20for%20attendance%20system.pdf https://eprints.ums.edu.my/id/eprint/30051/ https://beei.org/index.php/EEI/article/view/2859/2150 https://doi.org/10.11591/eei.v10i2.2859 |
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my.ums.eprints.300512021-07-23T03:56:36Z https://eprints.ums.edu.my/id/eprint/30051/ Real-time mask detection and face recognition using eigenfaces and local binary pattern histogram for attendance system Mohd Suhairi Md Suhaimin Mohd Hanafi Ahmad Hijazi Chung Seng Kheau Chin Kim On LB Theory and practice of education TA Engineering (General). Civil engineering (General) Face recognition is gaining popularity as one of the biometrics methods for an attendance system in an organization. Due to the pandemic, the common face recognition system needs to be modified to meet the current needs, whereby facemask detection is necessary. The main objective of this paper is to investigate and develop a real-time face recognition system for the attendance system based on the current scenarios. The proposed framework consists of face detection, mask detection, face recognition, and attendance report generation modules. The face and facemask detection is performed using the haar cascade classifier. Two techniques for face recognition were investigated, the eigenfaces and local binary pattern histogram. The initial experimental results and implementation at Kuching Community College show the effectiveness of the system. For future work, an approach that is able to perform masked face recognition will be investigated. Institute of Advanced Engineering and Science (IAES) 2021-02-20 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/30051/2/Real-time%20mask%20detection%20and%20face%20recognition%20using%20eigenfaces%20and%20local%20binary%20pattern%20histogram%20for%20attendance%20system-Abstract.pdf text en https://eprints.ums.edu.my/id/eprint/30051/1/Real-time%20mask%20detection%20and%20face%20recognition%20using%20eigenfaces%20and%20local%20binary%20pattern%20histogram%20for%20attendance%20system.pdf Mohd Suhairi Md Suhaimin and Mohd Hanafi Ahmad Hijazi and Chung Seng Kheau and Chin Kim On (2021) Real-time mask detection and face recognition using eigenfaces and local binary pattern histogram for attendance system. Bulletin of Electrical Engineering and Informatics, 10 (2). pp. 1105-1113. ISSN 2089-3191 (P-ISSN) , 2302-9285 ( E-ISSN) https://beei.org/index.php/EEI/article/view/2859/2150 https://doi.org/10.11591/eei.v10i2.2859 |
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LB Theory and practice of education TA Engineering (General). Civil engineering (General) Mohd Suhairi Md Suhaimin Mohd Hanafi Ahmad Hijazi Chung Seng Kheau Chin Kim On Real-time mask detection and face recognition using eigenfaces and local binary pattern histogram for attendance system |
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Face recognition is gaining popularity as one of the biometrics methods for an attendance system in an organization. Due to the pandemic, the common face recognition system needs to be modified to meet the current needs, whereby facemask detection is necessary. The main objective of this paper is to investigate and develop a real-time face recognition system for the attendance system based on the current scenarios. The proposed framework consists of face detection, mask detection, face recognition, and attendance report generation modules. The face and facemask detection is performed using the haar cascade classifier. Two techniques for face recognition were investigated, the eigenfaces and local binary pattern histogram. The initial experimental results and implementation at Kuching Community College show the effectiveness of the system. For future work, an approach that is able to perform masked face recognition will be investigated. |
format |
Article |
author |
Mohd Suhairi Md Suhaimin Mohd Hanafi Ahmad Hijazi Chung Seng Kheau Chin Kim On |
author_facet |
Mohd Suhairi Md Suhaimin Mohd Hanafi Ahmad Hijazi Chung Seng Kheau Chin Kim On |
author_sort |
Mohd Suhairi Md Suhaimin |
title |
Real-time mask detection and face recognition using eigenfaces and local binary pattern histogram for attendance system |
title_short |
Real-time mask detection and face recognition using eigenfaces and local binary pattern histogram for attendance system |
title_full |
Real-time mask detection and face recognition using eigenfaces and local binary pattern histogram for attendance system |
title_fullStr |
Real-time mask detection and face recognition using eigenfaces and local binary pattern histogram for attendance system |
title_full_unstemmed |
Real-time mask detection and face recognition using eigenfaces and local binary pattern histogram for attendance system |
title_sort |
real-time mask detection and face recognition using eigenfaces and local binary pattern histogram for attendance system |
publisher |
Institute of Advanced Engineering and Science (IAES) |
publishDate |
2021 |
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https://eprints.ums.edu.my/id/eprint/30051/2/Real-time%20mask%20detection%20and%20face%20recognition%20using%20eigenfaces%20and%20local%20binary%20pattern%20histogram%20for%20attendance%20system-Abstract.pdf https://eprints.ums.edu.my/id/eprint/30051/1/Real-time%20mask%20detection%20and%20face%20recognition%20using%20eigenfaces%20and%20local%20binary%20pattern%20histogram%20for%20attendance%20system.pdf https://eprints.ums.edu.my/id/eprint/30051/ https://beei.org/index.php/EEI/article/view/2859/2150 https://doi.org/10.11591/eei.v10i2.2859 |
_version_ |
1760230712512872448 |
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13.211869 |