Facial Recognition Attendance Monitoring System

Attendance tracking is a crucial part of managing academic institutions, but traditional methods—like manual roll calls or card systems—often fall short. They can be inefficient, take up too much time, and are prone to errors and even cheating, such as students marking attendance for their friends. On the other hand, existing biometric solutions that rely on contact, like fingerprint or iris scanning, come with their own set of issues, including hygiene concerns, high hardware costs, and slow processing times for large groups of students. This paper introduces an innovative, contactless Facial Recognition Attendance System aimed at overcoming these challenges in a college setting. The system is designed as a mobile-first web application, allowing students to easily check in using their own smartphones. It utilizes open-source computer vision libraries, particularly [DeepFace] in combination with [OpenCV], to ensure accurate and efficient facial recognition. Additionally, the system makes use of browser-based Geolocation APIs and a backend geofencing module to enforce location-based attendance, confirming that students are actually on campus. A working prototype has been developed and tested on a small scale, showing promising results in real-time facial recognition and location verification through a standard mobile web browser. This project presents a practical and budget-friendly attendance solution that could greatly improve security, cut down on proxy attendance, and boost administrative efficiency in educational environments.