Class Attendance System Based on Face Recognition

Abstract

The conventional methodology for recording student attendance, which heavily relies on manual data transcription, is prone to inefficiencies and errors. Consequently, the development of an automated attendance management system has emerged as a critical need for efficient and accurate maintenance of attendance records. This study presents the design and implementation of an automated attendance management system, exploiting face recognition technology for identifying students within a class setting. A unique dataset was curated, consisting of 3900 facial images, captured in five varying positions and under diverse lighting conditions. In the initial phase of the system's operation, images of students are captured via a mobile camera. Subsequently, the Haar Cascaded classifier is utilized for the detection of faces within these captured images, and the FaceNet network is employed to recognize the detected faces. In the subsequent phase, the system registers attendance by cross-referencing the recognized faces with the primary student record. An attendance sheet copy is then dispatched to the teacher. Upon evaluating the system's effectiveness, it was ascertained that the system successfully identifies students and registers their attendance with an impressive accuracy of 97.5%. It outperforms traditional systems in terms of workload reduction, error avoidance, speed, and accuracy. The proposed system holds potential for widespread deployment in institutes and schools for recording attendance and could be extended for employee attendance recording. By reducing human errors and the time required for attendance registration, and by swiftly generating electronic attendance lists, this system signifies a substantial improvement over conventional systems.