Development of a multi-client student attendance monitoring system

Abstract

A multi-client student attendance student monitoring system was developed. The attendance system consists of the client and the server. The core functions of the client device are verifying student’s identity for attendance recording and monitoring their presence in class. Haar-feature based cascade classifier for object detection and the Scale Invariant Feature Transform Technique (SIFT) technique were implemented for the face authentication process. This paper highlights a full-fledge system architecture with face-based identification implemented on the Raspberry Pi 2 board as the client alongside with RFID authentication for initial identification. The system also has webpage integration for system management. The accuracy achieved was 84% for face verification and 75% for face recognition. The experimental result showed that the recognition rate was affected by inconsistency of wearing glasses, distance between the face and the webcam, lighting condition and the environmental background. A database was setup to store attendance and student information. It is supported with a web application to view, update and analyze the attendance data.