Multimodal Biometric Authentication: Fusing Face and Fingerprint Recognition for Intelligent Attendance Systems

Abstract

This paper documents the design and implementation of an intelligent multimodal attendance and authentication system that combines facial recognition and fingerprint verification using IoT devices, aimed at the well-known weaknesses of manual roll calls, paper registers, and RFID cards in academic settings. The hardware consists of an ESP32-CAM for facial capture and a separate ESP32 board paired with an AS608 optical fingerprint sensor. Both report to a Laravel 13 web application responsible for student records, course enrolment, attendance sessions, and device management. Development was iterative: facial recognition was first tested against the system webcam before the ESP32-CAM was integrated, with the fingerprint module added afterward. The system was thoroughly tested to confirm that the core attendance objectives were met. Results confirmed that the combined facial and fingerprint approach addresses the proxy attendance problem and removes the manual overhead that paper-based systems impose. Keywords: Biometric Authentication, Facial Recognition, Fingerprint Verification, manual Overhead, Attendance Management System, ESP32 CISDI Journal Reference Format Abiola Oladimeji Abiola, Babalola Ayomide Isaiah, Adeyemi Favour Adesoji , Joel Joshua & Odufuwa Jesufunminire Emmanuel (2026): Multimodal Biometric Authentication: Fusing Face and Fingerprint Recognition for Intelligent Attendance Systems. Computing, Information Systems, Development Informatics & Allied Research Journal. Vol 17 No 3, Pp 23-40. Available online at www.isteams.net/cisdijournal. dx.doi.org/10.22624/AIMS/CISDI/V17N3P2