Classroom attendance checking system with facial detection and recognition

https://doi.org/10.70228/ CBJ2024054

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ABSTRACT

Traditional methods of checking attendance, such as the roll call method, consume more time and effort. Thus, the growing demand for efficient and accurate attendance monitoring systems in educational institutions has led to the development of innovative technological solutions. This study aims to automate the recording of attendance using facial recognition technology by designing and implementing the Classroom Attendance Checking System with Facial Detection and Recognition (CACSFDR) system at the University of St. La Salle. Employing an agile software development lifecycle and experimental methodology, the prototype integrates a high-resolution 4K camera, facial identification algorithms, and Python programming. The researchers included 20 participants to evaluate the performance of the system in real classroom settings, focusing on the accuracy of facial detection and recognition, the effectiveness of the system in reducing attendance checking time, and the effectiveness in managing attendance data. Results demonstrated a high level of accuracy in detecting and recognizing registered student faces and significantly decreased attendance checking time compared to traditional methods of taking attendance. The system provides reliable data management, corrects student status, ensures real-time tracking, and minimizes the risk of errors. The implementation of the Radio Frequency Identification module for secure access to hardcopy records further improves data security. CACSFDR effectively addresses the challenges of manual attendance tracking and promotes a more dynamic and interactive educational environment. The implementation of such technology not only streamlines administrative tasks but also enhances student participation in class and improves their academic performance.

Keywords: Automated attendance, image processing, machine learning, facial detection, facial recognition