INTELLIGENT FACE TRACKING ATTENDANCE SYSTEM USING LBPH AND KALMAN FILTERING - Jai; Abrol, Kuber; Saxena, Stuti (International Journal of Research -GRANTHAALAYAH, 2024)
Attendance tracking remains a crucial daily task in educational institutions and corporate environments. Traditionally performed manually, this process is often time-consuming and error-prone. To modernize and streamline attendance management, this project proposes a Facial Recognition-based Automated Attendance System that utilizes real-time image processing enhanced with a Kalman Filter for robust face tracking. The system captures and identifies student faces using a Logitech C270 webcam connected to an NVIDIA Jetson Nano Developer Kit. Initial face detection is performed using the Haarcascade classifier, followed by facial recognition through the LBPH (Local Binary Pattern Histogram) algorithm. The integration of the Kalman Filter enables smooth and continuous tracking of facial features, compensating for occlusions, motion blur, and abrupt student movements, thereby improving recognition accuracy and system responsiveness. The system maintains a dynamic attendance log by automatically cross-referencing recognized faces with a pre-trained dataset containing student details such as name, roll number, class, and section. Attendance records are updated hourly and stored in an Excel sheet accessible by the instructor. This approach ensures a contactless, efficient, and reliable solution for attendance monitoring in real-world classroom environments.