DEVELOPMENT AND IMPLEMENTATION OF A FACIAL RECOGNITION-BASED AUTOMATED ATTENDANCE SYSTEM FOR ENHANCING ACADEMIC MONITORING EFFICIENCY

This research explores a Facial Recognition Automated Attendance System (FRAAS) designed to
replace inefficient manual attendance tracking in educational settings. The system employs the Local
Binary Pattern Histogram algorithm within a Python/Django framework and integrates OpenCV for
real-time facial recognition.
FRAAS operates through three processes: enrollment (capturing facial data), recognition (comparing
live images to stored data), and attendance logging (recording successful matches). The
implementation prioritizes data security through encryption and access controls.
Performance testing with 50 students demonstrated 93.7% recognition accuracy in optimal lighting
(90.2% in variable conditions) with false acceptance rates below 1.5%. Recognition averaged 1.2
seconds per student versus 8-10 minutes for manual methods. User satisfaction reached 87% among
faculty and 79% among students.
Unlike commercial alternatives, FRAAS emphasizes cost-effectiveness and customizability for
academic environments. Future developments include mobile integration, machine learning
improvements, and expanded analytics for attendance pattern identification.
The research concludes that facial recognition offers a viable, efficient alternative to conventional
attendance systems, delivering significant benefits in efficiency, accuracy, and fraud prevention at
reasonable implementation costs.In today's rapidly advancing educational landscape, traditional attendance management methods are
becoming increasingly inefficient. Teachers and administrators often struggle with manual processes
that consume valuable time and introduce human error. As educational institutions embrace digital
solutions to streamline their operations, one area that stands to benefit significantly is attendance
tracking.
The Facial Recognition Automated Attendance System (FRAAS) addresses these challenges by
leveraging state-of-the-art facial recognition technology combined with machine learning algorithms.
This innovative system automates the attendance recording process in real time, offering educational
institutions an efficient, secure, and accurate solution. By capturing live video feeds, the system
identifies students' faces, matches them against the database, and marks their attendance without any
manual intervention.
FRAAS transforms the way educational institutions handle attendance, providing an error-free
experience for teachers, students, and administrators alike. By automating attendance management,
the system allows educational staff to focus on more critical tasks, thus improving overall efficiency
and reducing administrative burdens.