ClassVision: AI-Powered Classroom Attendance System

Students and working professionals have to go through the attendance process every day. Traditional methods of marking attendance using pen and paper or online platforms are human-intensive and time-consuming. To address the challenges in manual attendance processes, this research explores the use of face detection (FD) and face recognition (FR) technology to automate the attendance process, particularly in educational settings, and build a ClassVision course attendance system. We also propose an automated attendance system featuring a human-computer interaction (HCI) and user-friendly web interface that utilizes real-time image processing to identify and recognize students in classrooms and automatically record their attendance. We identified RetinaFace as the best face detection model, and when combined with Face Recognition for verification, it provided the most promising results with a cropped embedding of 50×50 pixels. Our proposed framework achieves a detection accuracy of 99.4% and a recognition accuracy of 90.3%. Unlike deep learning methods that require students to be present closely in front of the camera to capture attendance, our pioneering research aims to capture attendance seamlessly during ongoing classes, redefining the framework of attendance tracking systems. Source code is available at https://tinyurl.com/Course-Attendance-Robot.