Attendance Management using Deep Face Detection and Recognition

Traditional attendance systems depend on manual roll call methods together with ID card and fingerprint scanner systems which create operational delays that lead to attendance errors because students can enter class without their attendance being recorded. This project creates an Attendance Management System which achieves attendance tracking through Face Detection and Recognition technology that uses computer vision and machine learning processes to complete its functions. The system establishes real time video streaming through a camera system which uses face detection algorithms to identify human faces. The facial features which the system extracts undergo processing through a trained face recognition model which matches individual faces against a previously stored database. The system records student attendance automatically after it identifies a match in the input of the student name and date and time to the database without any human intervention. The solution uses contactless technology which requires minimal processing time and provides reliable outcomes to achieve result accuracy. The system allows users to handle student records while producing attendance reports because they can convert data into different assessment formats. The system functions in classrooms and laboratories and offices and all other institutional spaces. This project develops an attendance tracking system which operates on Python and OpenCV and deep learning facial recognition technology to create a secure and scalable attendance control mechanism which increases operational efficiency while decreasing cheating incidents.