Facial Recognition Attendance Monitoring Syst...

Facial Recognition Attendance Monitoring System Using Deep Learning With Yolov8

Author Name : Mrs. Saranya. S, Chandru .M, Ragul .S, Kafeel S A

ABSTRACT : Attendance management in educational institutions is a critical yet challenging task, often plagued by inefficiencies and inaccuracies associated with traditional methods. To address these issues, this paper proposes an advanced Smart Attendance System leveraging state-of-the-art face recognition technology integrated with the YOLOv8 (You Only Look Once version 8) algorithm for real-time object detection. This system employs a comprehensive approach, utilizing machine learning and deep learning techniques including MT-CNN, VGGFace2, and YOLOv8 to achieve precise face detection and recognition, even in offline mode. Unlike conventional attendance systems, our solution not only records attendance but also evaluates students' attentiveness during lectures using advanced metrics such as Eye Aspect Ratio (EAR), Mouth Aspect Ratio (MAR), and Gaze Angle, providing valuable insights into lecture effectiveness and student engagement. Security and privacy are paramount, with robust encryption and authentication mechanisms ensuring data integrity and confidentiality. Performance enhancements through model optimization, data augmentation, and hardware acceleration further contribute to the system's efficacy. By automating attendance management and leveraging cutting-edge technology, our Smart Attendance System aims to revolutionize educational institutions' administrative processes, fostering accountability, improving learning outcomes, and adapting to evolving educational needs.