ABSTRACT

The Face Recognition Attendance System is a cutting-edge solution that harnesses the power of deep learning and biometric technology to revolutionize attendance management in various organizational settings. This project explores developing and implementing a robust, automated system that accurately records attendance using facial recognition algorithms. This system offers enhanced security, efficiency, and real-tune monitoring by eliminating the need for manual attendance registers or swipe cards. The project begins by providing an overview of the motivation behind adopting face recognition technology for attendance management. Traditional methods often suffer from issues such as buddy punching and time fraud, leading to inaccurate attendance records. Face recognition algorithms address these challenges by uniquely identifying individuals based on their facial features, ensuring the authenticity of attendance data. The tools used are Python programming language, a laptop with its camera, a face recognition module, and OpenCv. Our methodology for developing the Face Recognition Attendance System is characterized by a structured approach reminiscent of the Waterfall model, ensuring comprehensive project planning, execution, and meticulous testing. The steps include Project Initiation, System Design, Implementation, Testing and Quality Assurance, Deployment Monitoring, and Maintenance.