Evolution of Attendance Systems: From Manual to Automate

Abstract

This paper proposes an automated attendance management system that influences face detection and recognition technologies to streamline attendance tracking in educational environments. The system automatically identifies students as they enter the classroom and marks their attendance by recognizing their faces. This removes the need for manual attendance-taking, saving valuable time during class sessions. The proposed system consists of several components, each involving key face detection and recognition algorithms. When a student enters the classroom, the system captures their facial features using a camera and then processes the image to detect the student's face. After the detection phase, facial recognition algorithms match the captured image with the pre-registered student profiles. If the system identifies a match, attendance is automatically recorded. We consider various real-time scenarios to evaluate the system's performance, including different lighting conditions and facial orientations. The paper discusses how different face recognition algorithms perform under these conditions, helping to identify the most efficient approach. Additionally, the paper addresses potential threats such as spoofing, where someone may attempt to manipulate the system by using a photograph or video of a registered student. Techniques like liveness detection are proposed to counter these threats and ensure the system's security and reliability.