SMART ATTENDANCE TRACKING SYSTEM EMPLOYING DEEP LEARNING FOR FACE ANTI-SPOOFING PROTECTION

Abstract

Conventional attendance systems face challenges in accuracy and efficiency, often vulnerable to spoofing and data manipulation. This study addresses these issues by developing a smart attendance system integrating Deep Learning-based facial recognition with anti-spoofing technology. The system ensures secure and reliable attendance authentication while automating and enhancing management processes. Utilizing a convolutional neural network (CNN) architecture, the system processes raw facial images directly without additional feature extraction, improving accuracy and efficiency. A novel training strategy, termed 50 Random Samples-30 Sub-epochs Count-1 Epoch, is introduced to optimize the training process. This strategy involves random sampling during each forward pass and grouping 30 passes as one epoch, enabling the use of complex CNN architectures and automatic dataset expansion. The system achieves 98.90% accuracy in identifying genuine attendance, maintaining a confidence level above 80%, significantly reducing spoofing risks and errors. This innovative solution has significant implications, particularly for educational institutions. It automates attendance tracking, minimizes manual effort, reduces errors, and supports disciplinary enforcement through accurate data. Moreover, its scalability allows for application across various environments, offering benefits to a wide range of institutions. By enhancing data accuracy and operational efficiency, this system sets a foundation for smarter, more reliable attendance management, strengthening administrative practices in education and beyond.