DESIGN AND IMPLEMENTATION OF RECOGNITION SYSTEM FOR STUDENTS ATTENDANCE

ABSTRACT This project developed a face detection and recognition system using Python, leveraging the OpenCV and face_recognition libraries. The primary objective was to create a robust and efficient system capable of real-time face detection and recognition for practical applications such as attendance tracking and security. The system utilized the Viola-Jones algorithm for face detection, SIFT (Scale-Invariant Feature Transform) for feature extraction, and Eigenfaces for face recognition. The choice of these algorithms was driven by their balance of accuracy and computational efficiency, suitable for the project's scope and resource constraints. The implementation was carried out in a console-based application, which, while functional and well-received for its simplicity, highlighted the potential for improvement through a graphical user interface (GUI). The system’s performance was evaluated through extensive testing, revealing effective real-time processing but also identifying areas for enhancement in accuracy and user experience. Data was stored in a CSV format, providing a straightforward solution for managing face images and recognition results. The project confirms the feasibility of creating a basic face recognition system using accessible Python libraries and suggests avenues for future work, including GUI development, integration with access control systems, and performance testing under varied conditions. These recommendations aim to refine the system and expand its practical applications.