DESIGN AND IMPLEMENTATION OF A FACE RECOGNITION AND FINGERPRINT AUTHENTICATION SYSTEM FOR CURBING EXAMINATION IMPERSONATION

This project focuses on the Design and Implementation of a biometric-based identification system that combines face recognition and fingerprint authentication to enhance examination security, using Abubakar Tafawa Balewa University, as a case study. The goal is to eliminate the widespread issue of examination impersonation and replace the existing manual student verification process, which requires student to generate exams cards, seek departmental signatures, and undergo inefficient identity checks-processes that are often stressful, time- consuming, and prone to manipulation. The system is developed using a Raspberry Pi 3 as the central processing unit, equipped with a Raspberry Pi Camera Module v2 (8MP) for facial recognition and a fingerprint sensor (R305 or GT-511C3) for fingerprint verification. Additional hardware includes a 3.5-inch 12C OLED display for system feedback, breadboard and jumper wires for prototyping connections, a 5v 2.5A power supply, a 16GB MicroSD card for OS and data storage, and an enclosure to house the device. On the software side, the system runs on Raspberry Pi OS and is programmed using python, utilizing OpenCV for image processing and face recognition, and a fingerprint recognition library for biometric matching. MySQL is used to store student data and biometric templates. By combining dual-biometric authentication, this system ensures accuracy of 91.2%, secure (i.e matching was generally consistent across user about 93.3%) and fast (average response time of 1.32 second) identification of students, effectively preventing impersonation and streamlining the verification process. The use of Abubakar Tafawa Balewa University as a case study underscores the solution’s real-world applicability and potential for adoption across other