STAFF ATTENDANCE MONITORING SYSTEM USING FACIAL AUTHENTICATION

ABSTRACT This project focuses on the design and implementation of a Staff Attendance Monitoring System using Facial Authentication, aimed at automating and enhancing the accuracy, efficiency, and reliability of employee attendance tracking. The system leverages facial recognition and facial emotion detection technologies integrated with a centralized database to eliminate manual errors, reduce time wastage, and ensure secure data management. Using the Rapid Application Development (RAD) methodology, the system was developed through iterative prototyping involving user feedback and testing. The backend was implemented using Python, TensorFlow, and OpenCV, while the front-end interface supports real-time video capture for facial analysis and authentication. The system was tested for accuracy, performance, and user acceptance, with successful results in face detection, recognition, and attendance logging. Findings demonstrated that the use of deep learning algorithms, particularly Convolutional Neural Networks (CNNs), significantly improved facial authentication accuracy and system responsiveness. This system provides an effective solution for organizations seeking an intelligent, automated, and emotion-aware approach to attendance management. Keywords: Facial Authentication, Attendance Monitoring, Deep Learning, Emotion Recognition, Rapid Application Development (RAD).