OPTIMIZING THE INTEGRATION OF FINGERPRINT AND FACIAL RECOGNITION TECHNOLOGIES FOR REAL-TIME ATTENDANCE SYSTEMS

Abstract

The integration of multiple biometric modalities, specifically fingerprint and facial recognition technologies, has gained significant attention in enhancing the accuracy, security, and reliability of real-time attendance systems. This paper explores the optimization of a multi-biometric attendance system that combines fingerprint and facial recognition to provide a robust, user-friendly, and efficient solution for attendance tracking. The proposed system leverages the strengths of both technologies, where fingerprints offer high accuracy for identification and facial recognition adds an additional layer of security, reducing the risk of spoofing and unauthorized access. Key challenges in integrating these technologies, such as data fusion, synchronization, and real-time processing, are addressed through optimized algorithms designed to handle large-scale user data and ensure fast identification. Performance evaluations demonstrate improved accuracy, reduced false acceptance and rejection rates, and faster processing speeds compared to single-modal systems. The results also highlight the potential of this hybrid approach for deployment in diverse environments, including educational institutions, corporate settings, and event management. This study offers valuable insights into the practical application of multi-biometric systems, providing a foundation for future advancements in attendance management technologies.