Development of Student Face Recognition Attendance System

Face recognition technology, a subset of biometric technology, has emerged as a crucial and revolutionary tool in various industries, including identity verification, security, and surveillance. In this study, face recognition technology has been employed to enhance the student attendance system at the Faculty of Computer Science and Information Technology (FSKTM). The current attendance recording method, which requires students to scan a QR code during teaching and learning sessions, presents several challenges. It is time-consuming, prone to inaccuracies, and susceptible to misuse, such as remote attendance recording and QR code sharing, which can lead to errors and class disruptions. To address these issues, the Student Face Recognition Attendance System was developed to ensure attendance is recorded accurately, efficiently, and with integrity. The system incorporates three user roles, which are administrator, students, and lecturer, and focuses on managing student attendance data within educational institutions. Prototyping software process model was adopted for this project, emphasizing iterative development through phases including requirement gathering, quick decision-making, building and refining a prototype, user evaluation, and final product development. The system was developed using a combination of OpenCV and software such as PyCharm and Firebase, with Python as the programming language. This system is expected to significantly reduce the time required for attendance recording while minimizing errors, thereby improving the overall efficiency and reliability of the attendance process.