Class Attendance System using Unimodal Face Recognition System based on Internet of Educational Things

Abstract

This study presents the design and implementation of an IoT-based class attendance system with face recognition for the high school. The system includes the Open-Computer-Vision (OpenCV) library, Python programming language, and a Raspberry Pi as the main processing unit. The system employs a combination of Haar-Cascades for face detection and Eigenfaces, Fisher faces, and Local Binary Pattern Histograms for face recognition. The methodology for the system is described in detail, including flowcharts for each stage of the system. The experiment results of the system are analyzed and presented, including plots and screenshots. We discussed the challenges encountered during the project, the system's potential applications, and future developments. The system was developed to automate the attendance-taking process, increase the accuracy and security of attendance records, and provide data to improve student performance and progress by recording class attendance in Google Sheets.