Real-Time Multi-Face Detection for Automated Attendance Management Using YOLOv8

Abstract

Accurate and efficient attendance management remains challenging in classroom environments because face-based systems are sensitive to illumination, pose variation, occlusion, and simultaneous student entry. This study proposes a YOLOv8n-based attendance management framework that integrates entropy-guided frame selection, automated face annotation, classroom-oriented augmentation, closed-set student identification, and temporal attendance validation. The dataset comprised 1,800 frames from six enrolled students and was expanded to 12,600 training images through augmentation applied exclusively to the training partition. The model achieved a precision of 0.620, recall of 0.692, mAP@0.5 of 0.566, and mAP@0.5:0.95 of 0.497 on the held-out test set. Model-only inference required 1.344 ms per image under the specified computational configuration, whereas the complete attendance pipeline, including frame acquisition, preprocessing, detection, rendering, and attendance logging, required an average of 0.122 s per frame, corresponding to approximately 8.2 FPS. Temporal confirmation and check-in/check-out rules prevented short-lived or repeated detections from generating duplicate attendance records. These findings demonstrate the feasibility of the framework as a proof of concept for controlled, closed-set classroom attendance involving a limited cohort. Further evaluation with more participants, independent recording sessions, and cross-environment data is required before broader deployment can be claimed.