Two-Step Attendance System with RFID and Facial Recognition

This research study intends to design a Two-Step Attendance System that combines RFID, facial recognition, blockchain-based databases, and edge-based machine learning to improve the accuracy, security, and efficiency of attendance management. The system initially employs RFID technology for preliminary identification and then real-time facial recognition on edge devices to confirm the identity of the person. To provide tamper-proof recordkeeping, attendance information is kept in a blockchain-based database that is resistant to unauthorized alterations and eliminates schemes like buddy punching and identity spoofing. The use of edge-based machine learning minimizes reliance on centralized cloud servers to provide real-time authentication with negligible latency. Through the utilization of a multi-factor authentication mechanism, the suggested system improves scalability, reliability, and privacy and can be applied in educational institutions, corporate workplaces, and secure environments. The research adds value to the domain by showcasing a new combination of biometric authentication, decentralized storage, and AI-based real-time processing, creating a standard for next-generation attendance management systems.