International Journal of Scientific Research and Engineering Development

Automated attendance management is an essential administrative task in educational institutions and organizations, where accuracy, efficiency, and reliability are critical. Traditional methods, such as manual registers and RFID cards, often suffer from errors, proxy attendance, and time delays. The proposed Face Recognition Attendance System provides a contactless and automated solution using classical computer vision and machine learning techniques. Implemented in Python, the system uses OpenCV’s Haar Cascade classifier for real-time face detection and the Local Binary Patterns Histograms (LBPH) algorithm for face recognition, which learns and matches facial patterns for identification. Tkinter offers an intuitive graphical interface for desktop interaction, while Flask manages backend functionalities, including attendance logging and report generation. The system operates in two modes—Registration, for capturing and storing face data, and Attendance, for real-time verification. Evaluation demonstrates robust performance under varying lighting conditions and facial orientations, highlighting its applicability as a secure, efficient, and scalable solution for modern attendance management.