Attendance Using Face Recognition

Abstract

Attendance management is an important aspect of any educational or organizational setting. Traditional methods of attendance management, such as manual attendance or the use of biometric systems, are time-consuming and error-prone. In this project, we propose an attendance management system that uses face detection technology to mark attendance. The system uses cameras and facial recognition software to identify staff in real time and mark attendance status. Compare captured images with pre-stored images in the database and mark identified individuals for participation. The proposed system has several advantages, including improved accuracy and speed of attendance marking, reduced human intervention, and the ability to track attendance data in real time. The system can also generate reports that provide information on attendance trends and identify absentees.
We have implemented a face detection system using the Python programming language and the OpenCV library. We tested the system on a student dataset and achieved over 90% accuracy. Overall, the proposed attendance management system using face detection technology has the potential to revolutionize the way attendance is marked in educational and organizational settings.