DEVELOPMENT OF A BIOMETRIC (FINGERPRINT) STUDENT ATTENDANCE SYSTEM USING GLOWWORM SWARM OPTIMIZATION
Abstract
This study presents the development of a biometric fingerprint-based student attendance system enhanced with Glowworm Swarm Optimization (GSO). Traditional attendance methods are prone to impersonation, time inefficiency, and data inaccuracies. The proposed system integrates fingerprint recognition technology with GSO to optimize feature matching and improve identification accuracy. The system was designed using a structured software development approach and implemented with a fingerprint sensor, database, and optimization algorithm. Experimental results show improved speed and accuracy in attendance marking compared to conventional systems. The system effectively eliminates proxy attendance and enhances record management. This research demonstrates the applicability of swarm intelligence in biometric systems for academic environments.
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