Accurate and tamper-proof attendance monitoring is a challenge in educational, corporate and administrative settings. Existing systems that are solely dependent on manual entry, RFID card or biometric authentication with single- mode are often vulnerable to manipulation, impersonation and inconsistency of data. This study presents the design and implementation of a multimodal attendance system that integrates radio frequency identification (RFID), face recognition, fingerprint biometric and GSM-based SMS notifications to improve reliability, safety and accountability. The proposed system uses an ESP32 microcontroller to coordinate multiple hardware modules, including an RC522 RFID reader, an AS608 fingerprint sensor, an ESP32-CAM for facial recognition, and a SIM800L GSM module for mobile communication. Upon RFID approval, the system performs sequential fingerprints and face recognition, and ensures identity confirmation through at least two biometric modalities. Successful verification triggers real-time logging, timestamp and an SMS notification to a designated recipient. Experimental validation was performed using a test population, and the results show improved accuracy, low false acceptance/rejection rate and real-time responses. The system supports local data storage and provides scalability for integration with cloud -based platforms. This research contributes to the development of secure, efficient and user-centric attendance management systems through the deployment of multimodal biometric technologies.
Oloniyo et al. (Tue,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: