AI-Powered Smart Attendance & Anti-Spoofing Facial Recognition
Real-time multi-face attendance logging with liveness anti-spoofing detection using YOLOv8, FaceNet embeddings, and automated faculty analytics dashboard.
Detailed Project Overview
This project implements an automated multi-camera classroom attendance management system powered by deep learning. It uses YOLOv8 for sub-millisecond face bounding box localization and FaceNet for 512-dimensional facial feature embeddings. An integrated infrared/texture liveness detection module blocks spoofing attempts via printed photos or mobile screen playback. Includes a comprehensive Flask + React analytics dashboard for real-time Excel generation and automated absence SMS alerts.
Academic Problem Statement
Manual roll-call methods waste 15-20 minutes of instructional time per lecture and are prone to proxy attendance. Existing biometric fingerprint readers cause physical bottlenecks and hygiene concerns. A contactless, multi-person simultaneous facial recognition system with anti-spoofing is needed for modern academic institutions.
Proposed Methodology & Flow
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