International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Traditional manual attendance systems in academic institutions suffer from operational deficiencies, temporal consumption, and vulnerability to "proxy" attendance. This paper proposes the Smart Facial Recognition Attendance System, an interaction-free biometric solution leveraging computer vision and machine learning. The system utilizes MediaPipe for high-precision extraction of 468 facial coordinates, ensuring robustness against cranial orientation fluctuations. A Random Forest Classifier is employed for identity categorization based on extracted facial embeddings, achieving a recognition precision surpassing 95% under regulated conditions. Integrated via a Flask web architecture and SQLite database, the framework provides real-time monitoring and automated report generation in CSV format. Experimental results indicate a significant reduction in administrative overhead and enhanced data integrity for institutional governance.

Keywords: Facial Recognition, MediaPipe, 468 Facial Landmarks, Random Forest Classifier, Automated Attendance System, Flask Web Framework, Computer Vision, Biometric Authentication, Real-time Monitoring, Machine Learning

Downloads: | DOI: 10.17148/IJARCCE.2026.15175