ClassVision: AI-Powered Classroom Attendance System

Abstract: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 50x50 pixels.

Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)

arXiv:2608.26173 [cs.CY]

(or arXiv:2608.26173v1 [cs.CY] for this version)

https://doi.org/10.48550/arXiv.2608.26173 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

Proc. 2024 Fourth International Conference on Digital Data Processing (DDP), 2024, pp. 27-34

https://doi.org/10.1109/DDP64453.2024.00015 Focus to learn more DOI(s) linking to related resources

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