This research presents the implementation and design of a facial recognition attendance management system by using Local Binary Pattern Histogram (LBPH) and Haar Cascade machine learning algorithms. This system aims to automate the attendance process, providing an efficient and accurate alternative to traditional methods. The Haar Cascade algorithm is employed for face detection due to its rapid processing and high detection rate, while the LBPH algorithm is utilized for face recognition because it is simple and effective in handling changes in facial expressions, lighting, occlusions, distance from the camera, and camera resolution. The integration of these algorithms results in a robust system capable of real-time attendance tracking. Experimental results demonstrate the system's high accuracy and reliability in face detection and recognition under different conditions of lighting, distance from the camera, face expressions, occlusions, and camera resolution, making it suitable for deployment in educational institutions and corporate environments.

Facial Recognition Haar Cascade Attendance Management Local Binary Pattern Histogram Machine Learning Real-time Tracking