AutoCAS: An Automated Classroom Attendance System using Machine Learning

Marking attendance during classes using traditional methods is a tedious and error-prone process. This conventional approach wastes valuable teaching time and can cause errors that affect student records. To address these challenges, we propose an automated attendance system with advanced facial recognition and detection techniques to streamline the process. Our system uses DeepFace for both face detection and recognition, selected after careful comparison with methods such as Haar cascades and YOLOv9. Among the approaches tested, DeepFace demonstrated the highest reliability and precision, effectively identifying students from a single snapshot of the classroom. This automated solution reduces the time and manual effort involved in attendance, making the process more efficient and less intrusive. Our system offers a reliable and user-friendly solution for educators, improving routine classroom operations with minimal disruption.