REAL-TIME IMPLEMENTATION OF AN AUTOMATED STUDENT ATTENDANCE MONITORING SYSTEM

Abstract: The efficient management of student attendance is a persistent administrative challenge in educational institutions. Conventional manual and token-based attendance systems are time-consuming, error-prone, and vulnerable to proxy attendance. Although biometric solutions such as fingerprint scanners address identity verification, they introduce hygiene concerns and operational bottlenecks. This paper presents a real-time, contactless, and fully automated student attendance monitoring system based on computer vision and deep learning techniques. The proposed system integrates YOLOv8 for high-speed face detection with the VGG-Face model for robust facial recognition. A novel duration-based attendance validation mechanism is introduced, wherein a student is marked present only after being continuously or cumulatively recognized for a predefined duration. The system further automates attendance reporting through Excel generation and real-time email notifications using SMTP. Experimental evaluation demonstrates high accuracy, robustness to occlusion and lighting variations, and suitability for real-world classroom deployment.

Keywords: Automated Attendance, Face Recognition, YOLOv8, VGG-Face, Deep Learning, Computer Vision