Enhanced Classroom Management: A Smart AI-Based Attendance System Utilizing YOLOv11 for Face Recognition

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P. Rishik; K. Megha sri; M. Srimann Reddy; Mr. Vaggela Rama Chandra Murthy Raju

Enhanced Classroom Management: A Smart AI-Based Attendance System Utilizing YOLOv11 for Face Recognition

Authors: P. Rishik, K. Megha sri, M. Srimann Reddy, Mr. Vaggela Rama Chandra Murthy Raju

Unique Paper ID: 176290
Volume: 11
Issue: 11
PageNo: 6318-6325

Keywords: AI-based attendance system face recognition YOLOv11 real-time object detection deep learning.

Abstract:
In today’s educational landscape, effective classroom management is crucial for promoting student engagement. Traditional attendance methods, such as roll calls and biometric systems, often lead to inefficiencies and errors. To address these issues, this study presents an AI-driven solution, "Enhanced Classroom Management: A Smart AI-Based Attendance System Utilizing YOLOv11 for Face Recognition." By leveraging advanced object detection, this system automates attendance tracking, reducing manual effort and improving accuracy. [1] The proposed system uses YOLOv11, a deep learning model known for its speed and precision in real-time object detection. Unlike biometric systems that require physical interaction, this technology enables automatic facial recognition for attendance recording. The AI-based framework enhances efficiency, ensuring real-time processing while minimizing disruptions during lectures. [2] Evaluations show that the YOLOv11-powered system improves accuracy and reduces administrative workl oad. The findings highlight its potential to enhance security and reliability in attendance management. This research contributes to the growing field of AI in education, demonstrating how intelligent systems can create a more accountable learning environment.[3]

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 11
PageNo: 6318-6325

Enhanced Classroom Management: A Smart AI-Based Attendance System Utilizing YOLOv11 for Face Recognition

Available:https://ijirt.org/article?manuscript=176290

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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