Real Time Attendance Capturing through Face Recognition

This application proposes the implementation of face recognition technology to enhance classroom attendance tracking. It involves two main steps: First is Face detection and the other is Face recognition. In face detection, the system identifies the locations of faces within classroom images and extracts face sub-images. The faces detected are then processed with an existing database of student images for recognition, leading to the recording of attendance. Unlike conventional methods, the model utilizes real-time video for attendance tracking, making it more efficient and accurate. The main objective of this approach is to speed up the process of taking attendance even when used in a crowded area in real-time unlike other existing systems. The Deep Learning Model employs machine learning tools like OpenCV, Dlib, and CNN to achieve its goals. This real-time, technology-driven approach aims to modernize attendance recording in educational settings.