Hybrid Deep Learning based Smart Attendance System with Robust Anti Spoofing Mechanism

Abstract

This paper introduces an innovative smart attendance system that employs deep learning-based face recognition, seamlessly integrated with advanced anti-spoofing techniques, to deliver secure and dependable attendance management for both educational and corporate settings. The system leverages a refined hybrid algorithm, optimized through parameter tuning, to enhance recognition accuracy and effectively mitigate fraudulent attempts. Evaluated on standard benchmark datasets (indexed in SCIE and Scopus), the system demonstrates superior real-time performance. Experimental results reveal high accuracy, fast processing, and strong resilience against spoofing attacks. As face recognition becomes an increasingly prominent biometric authentication method in security-critical environments, it remains vulnerable to spoofing threats such as printed photographs and video replay attacks, a challenge this work directly addresses. This research proposes a Smart Attendance System enhanced with an anti-spoofing module combining convolutional neural networks (CNN), temporal modelling using LSTM, motion and blink detection, and robust face recognition via FaceNet and SVM. We employ datasets including CASIA-FASD to construct and fine-tune our model. Experimental evaluation confirms our system achieves high accuracy with significant spoof-resistance. This integrated approach ensures reliability for real-time applications like institutional attendance, banking security.