Face recognition based attendance system using machine learning with location identification

Abstract

The paper argues that maintaining regular attendance is crucial for student success, and traditional attendance management methods can be inefficient and time-consuming for teachers and administrators. For example, calling out student names or taking manual attendance on paper can take up valuable classroom time and can be prone to errors or manipulation .To address these issues, the paper suggests that a computer-based attendance management system using Computer Vision technology can be an effective solution. Computer Vision involves the use of cameras, sensors, and algorithms to identify and analyze visual data, including images of individuals. In the context of attendance management, Computer Vision can be used to capture images of students during class and automatically recognize and mark their attendance using facial recognition technology. This approach can offer several advantages over traditional attendance methods. Firstly, it can be faster and more accurate, reducing the time and effort needed to manage attendance manually. Secondly, it can provide real-time updates on attendance status, allowing teachers to track students who arrive late or leave early. Finally, it can generate reports on attendance patterns, allowing administrators to identify and address issues related to student attendance and engagement. Overall, the paper highlights the potential benefits of using a computer-based attendance management system using Computer Vision, emphasizing its ability to streamline attendance management and improve student outcomes.