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All Journal Prosiding SNATIKA Vol 01 (2011) TELEKONTRAN: Jurnal Ilmiah Telekomunikasi, Kendali dan Elektronika Terapan JTEIN: Jurnal Teknik Elektro Indonesia International Journal of Engineering, Science and Information Technology JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) ELECTROPS : Jurnal Ilmiah Teknik Elektro
Wirawan, Adi Pandu
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Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Multiperson Automated Attendance System Based on Face Recognition Using YOLO and DeepFace with Active Learning Suprihanto, Didit; Wirawan, Adi Pandu; Saputra, Kahlil Gibran; Harjanto, Arif; Hakim, Imam Muhammad; Nugroho, Happy
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1772

Abstract

Accurate student attendance tracking is essential in academic environments, yet traditional methods remain inefficient and vulnerable to manipulation. This research presents a classroom attendance system based on facial recognition that integrates YOLO for multiperson face detection and the SFace model from the DeepFace framework for feature extraction and identity matching. A key contribution of this study is the implementation of an Active Learning mechanism that enables the system to update its embedding Database using user-provided corrections, enabling continuous adaptation to real classroom conditions. The system was developed as a Python-based desktop application and evaluated using 38 group images captured with various devices under uncontrolled lighting, diverse head poses, occlusion, and different classroom densities. Performance was assessed using accuracy, False Rejection Rate (FRR), and False Acceptance Rate (FAR) across two scenarios: before and after Active Learning. Experimental results show a substantial improvement after the learning process, with accuracy increasing from 52.0% to 96.6%, while maintaining a low FAR of 0%. These findings demonstrate that Active Learning effectively enhances recognition performance by enriching the embedding Database with real-world facial variations that do not present during initial registration. Overall, the proposed system highlights the importance of integrating Active Learning into face recognition–based attendance applications to improve robustness and adaptability in unconstrained multiperson classroom environments.
Co-Authors Alfarisi, Dafid Imam Alham, Nur Rani Arif Harjanto Brahmantya Aji Pramudita Budiasih, Alfit Fajri Burhandenny, Aji Ery Didit Suprihanto, Didit Fadilah, Fahri Fahrizal, Ahmad Jidan Hakim, Imam Muhammad Maxima Ari Saktiono Muhammad Hablul Barri Muslimin Muslimin Nugroho, Happy Rahmadhani, Rizal Rumawan, Fatkhul Hani Sakti, Bima Saputra, Kahlil Gibran Utomo, Muhammad Dodi Utomo, Restu Mukti Wahab, Aab Abdul