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Muhamad Naufal Azwar Iftikar
School of Computer Science, The University of Sydney, 1 Cleveland Street, Darlington, NSW 2008,
Computer Science & IT

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AI-based Efficient Hallway Attendance Monitoring using Confidence-driven Face Recognition Mokhamad Arfan Wicaksono; Ibrahim Faizal Burhan; Hadi Permana; Muhung Anggarawan; Bakhriyah Firdausi; Muhamad Naufal Azwar Iftikar; Ratnasari Nur Rohmah; Yoanes Bandung
Journal of ICT Research and Applications Vol. 20 No. 3 (2026)
Publisher : DRI - ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/itbj.ict.res.appl.2026.20.3.4

Abstract

Attendance monitoring deployed at entrance areas offers reliable tracking for institutional environments. We propose an efficient face-recognition-based framework combining YOLOv11-based face detection, motion-based tracking, and FaceNet-based recognition. Our key contribution is a confidence-driven face selection method that collects multiple face images per track and selects the highest-quality image for recognition. Experiments on real-world 60-second hallway footage (1,800 frames at 30 FPS) demonstrated up to 77.7% improvement in processing speed (79.36 FPS vs. 44.66 FPS baseline) while maintaining equivalent recognition accuracy. Collection windows of 5 to 7 images provide optimal balance between latency and throughput (73.67–75.86 FPS), demonstrating that strategic recognition frequency reduction enables real-time attendance monitoring suitable for resource-constrained environments
Co-Authors Bakhriyah Firdausi Hadi Permana Ibrahim Faizal Burhan Mokhamad Arfan Wicaksono Muhung Anggarawan Ratnasari Nur Rohmah Yoanes Bandung