Development of Face Recognition for Faculty Attendance Monitoring System

This study explores the development of a Face Recognition for Faculty Attendance Monitoring System at Tagoloan Community College, aimed at automating attendance tracking, enhancing accuracy, and providing real-time monitoring. The system integrates facial recognition technology with a web-based platform to record faculty attendance as they enter and exit classrooms. It captures real-time facial data, compares it with stored embeddings, and cross-checks faculty schedules to validate attendance. Key features include automated attendance logs, leave management, and comprehensive reporting tools for administrative use. The system's performance is assessed in terms of recognition accuracy, operational efficiency, and user satisfaction, while ensuring the integrity and security of stored data. Although it effectively automates the attendance process, limitations include reliance on pre-enrolled facial data and environmental factors that may affect recognition performance. Overall, the proposed solution provides a reliable, efficient, and scalable alternative to traditional manual attendance systems, supporting improved decision-making and transparency in faculty monitoring.