Adaptation of Face Recognition for Student Attendance in Distance Education

Nurbaity Sabri (), Anis Amilah Shari, Faiqah Hafidzah Halim, Zuhri Arafah Zulkifli and Hazrati Zaini
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Nurbaity Sabri: Universiti Tekologi MARA (UiTM), Kolej Pengajian Pengkomputeran, Informatik & Matematik
Anis Amilah Shari: Universiti Tekologi MARA (UiTM), Kolej Pengajian Pengkomputeran, Informatik & Matematik
Faiqah Hafidzah Halim: Universiti Tekologi MARA (UiTM), Kolej Pengajian Pengkomputeran, Informatik & Matematik
Zuhri Arafah Zulkifli: Universiti Tekologi MARA (UiTM), Kolej Pengajian Pengkomputeran, Informatik & Matematik
Hazrati Zaini: Universiti Tekologi MARA (UiTM), Kolej Pengajian Pengkomputeran, Informatik & Matematik

A chapter in Proceedings of the 10th Padang International Conference on Education, Economics, Business and Accounting (PICEEBA-10 2022), 2025, pp 667-678 from Springer

Abstract: Abstract The abrupt transition from traditional in-person education to remote online learning as a result of the COVID-19 pandemic had a significant impact on all parties involved, with students being particularly affected. A challenge arises in accurately assessing the levels and rates of student participation. There are limited methods available for monitoring student attendance particularly in the context of distance education. This study focuses on the utilisation of facial recognition technology to automate the process of attendance-taking, hence facilitating the participation of students in online classes offered through distance education. The present study employs Support Vector Machine (SVM) capacity to categorise photos into three distinct classes. The research commences with acquiring images and subsequently performing segmentation through the Graph-Based Segmentation technique. The Viola-Jones technique is employed for face detection, which is subsequently followed by feature extraction via the Local Binary Pattern (LBP) method. Ultimately, the process of face identification is achieved by the utilisation of the Support Vector Machine (SVM) methodology. The proposed methodology demonstrates a commendable level of recognition accuracy, with an 80.303% success rate when employing the SVM approach. Based on the obtained findings, it can be inferred that the implementation of facial recognition technology in the context of long-distance education holds promise as a viable solution to support the educational sector.

Keywords: Face Recognition; Distance Education; Student Attendance; Support Vector Machine (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-839-4_56

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DOI: 10.2991/978-94-6463-839-4_56

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