A Hybrid Attendance Monitoring Framework Using Fingerprint Authentication and FaceNet-SVM Recognition

This work proposes a hybrid attendance monitoring framework that combines mandatory fingerprint authentication with continuous CCTV-based facial recognition using FaceNet embeddings and an SVM classifier. Unlike conventional attendance systems that only verify a student at check-in, the proposed framework continuously measures classroom presence from video frames and determines final attendance using a predefined presence threshold. The dual-stage approach helps reduce proxy attendance while also identifying early departure or prolonged absence. The framework integrates face detection, preprocessing, FaceNet-based feature extraction, SVM classification, frame-level presence logging and automated attendance decision-making into a single practical system for classroom attendance monitoring.