Development of a Fingerprint-Biometric Expert System for Student Class Attendance Monitoring - Odulaja, Godwin Oluseyi (Faculty of Natural and Applied Sciences Journal of Computing and Applications, 2025)
Over the years, flaws such as impersonation, multiple entry, manipulation and mutilation of attendance records by unscrupulous students in various forms associated with the manual taking of class attendance for coursework and examination often compromise the accuracy and reliability of the records, thereby defeating the purpose of the exercise in Nigerian tertiary institutions. A Fingerprint-Based Student Attendance Monitoring System, FiBSAMS, was developed to mitigate against this problem in this study. An Atmega328P microcontroller, five fingerprint RS305 scanner modules, a liquid crystal display (LCD), RGB light emitting diodes, and a laptop computer were used to uniquely capture and validate fingerprints of 100 student candidates for populating the knowledge base of the expert system. With pattern matching algorithms, the inference engine (microcontroller) was configured and trained to validate every fingerprint input for acceptance or rejection. For 2 academic semesters, attendance was taken manually in the first semester and biometrically with FiBSAMS in the second semester. Results showed that the biometric attendance system had 95% successful valid and accurate identifications and 30% increase in class attendance relative to manual attendance records. Besides, manipulation of attendance records reduced significantly. Compared with manual attendance system, FiBSAMS’ performance amounted to a significant improvement in accuracy and reliability.