Face Detection and Recognition Student Attendance Systems

Abstract

Maintaining attendance manually is a challenging, time-consuming, and error-prone process. An intelligent and automated system for managing attendance can be implemented using various biometric technologies, with face recognition being one of the most influential and non-intrusive methods. In every organization, particularly educational institutions, accurate attendance tracking is essential to record the presence of individuals efficiently. Our face recognition project detects and identifies the faces of students, helping to save valuable time compared to traditional methods. Furthermore, the system generates a .csv file, allowing lecturers to maintain an organized, easily accessible daily attendance record. This not only ensures the accuracy of attendance tracking but also simplifies the analysis of attendance trends, promoting accountability and improving overall administrative efficiency. In addition, the automated system reduces the possibility of human error and tampering with attendance records. With its real-time tracking capabilities, the system can also provide instant reports and alerts, enhancing overall classroom management. By integrating face recognition technology, institutions can streamline operations, leaving more time for educational and developmental activities.