Attendance Management System Using Face Recognition

The development of this system is aimed at accomplishing the digitization of the traditional method of taking attendance, as present strategies for recording attendance are tedious and time-consuming, and records can be easily manipulated through manual entry. This paper presents an Attendance Management System using Face Recognition that automates the process of marking student attendance in real time using a standard webcam and computer vision techniques. The proposed system leverages Python 3.x, OpenCV, and the face_recognition library based on dlib to detect and identify student faces, subsequently logging attendance into a CSV/Excel file with timestamps. Experimental results on a dataset of 20-30 students demonstrate a face detection rate of 97.5%, a recognition accuracy of 95.8%, and an attendance logging success rate of 99.1%, significantly reducing administrative overhead and eliminating proxy attendance.