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- Vol. 6 Issue 6
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Smart Attendance and Student Monitoring System
Authors
1 Assistant Professor, Department of Information Science and Engineering, Bapuji Institute of Engineering & Technology, Davanagere, Karnataka, India. 2 3 4 5 Department of Information Science and Engineering, Bapuji Institute of Engineering & Technology, Davanagere, Karnataka, India.
Article Information
- Published In
- Volume 6, Issue 6 (2026)
- Published Online
- November-December 2026
- Pages
- 25-34
- Article Type
- Original Article
- Check for updates
-
References
1. A.Budiman, F. F. and R. A. Yaputera et al., “Student attendance with face recognition (LBPH or CNN): Systematic literature review,” Procedia Computer Science, vol. 216, pp. 31–38, 2023.
2. T. Gowda S., S. G., H. Supreetha, S. Zaiban, and H. K., “A Survey on Automated Student Attendance Management System Using Face Recognition,” Perspectives in Communication, Embedded-systems and Signal-processing.
3. A.S. Lateef and M. Y. Kamil, “Face Recognition-Based Automatic Attendance System in a Smart Classroom,” Iraqi Journal for Electrical and Electronic Engineering, 2023.
4. W. Wu, H. Peng, and S. Yu, “YuNet: A Tiny Millisecond-level Face Detector,” Machine Intelligence Research, vol. 20, pp. 656–665, 2023.
5. V. Bazarevsky, Y. Kartynnik, A. Vakunov, K. Raveendran, and M. Grundmann, “BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs,” CVPR Workshop on Computer Vision for Augmented and Virtual Reality, 2019.
6. Ultralytics, “Ultralytics YOLOv8,” 2023.
7. Ultralytics, “Object Detection with Ultralytics YOLO,” Ultralytics Documentation.
8. Google MediaPipe, “MediaPipe Face Mesh,” Google AI Edge, 2020.
9. H. Fang, S. Mu, X. Chen, J. Naz, et al., “Real-time classroom behavior and drawing pattern analysis using YOLOv11 for multi-class student activity detection,” Discover Computing, vol. 29, 2026.
10. Y. Li and Q. Chen, “A lightweight YOLOv11n-based framework for student behavior detection in smart classroom environments,” Discover Applied Sciences, vol. 8, article 86, 2026.
11. “Real-time Smartphone Usage Surveillance System Based on YOLOv5,” International Journal on Informatics for Development, 2023.
12. Revathy, A. Merisha, and V. Shivani, “AI-Enabled Smart Classroom: Monitoring Mobile Phone Usage with Real-Time Alerts,” 2nd International Conference on Global Trends in Engineering and Technological Advancement, 2025.
13. Python Software Foundation, “pyttsx3: Offline Text-to-Speech Library for Python,” Python Package Index, 2023.
2. T. Gowda S., S. G., H. Supreetha, S. Zaiban, and H. K., “A Survey on Automated Student Attendance Management System Using Face Recognition,” Perspectives in Communication, Embedded-systems and Signal-processing.
3. A.S. Lateef and M. Y. Kamil, “Face Recognition-Based Automatic Attendance System in a Smart Classroom,” Iraqi Journal for Electrical and Electronic Engineering, 2023.
4. W. Wu, H. Peng, and S. Yu, “YuNet: A Tiny Millisecond-level Face Detector,” Machine Intelligence Research, vol. 20, pp. 656–665, 2023.
5. V. Bazarevsky, Y. Kartynnik, A. Vakunov, K. Raveendran, and M. Grundmann, “BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs,” CVPR Workshop on Computer Vision for Augmented and Virtual Reality, 2019.
6. Ultralytics, “Ultralytics YOLOv8,” 2023.
7. Ultralytics, “Object Detection with Ultralytics YOLO,” Ultralytics Documentation.
8. Google MediaPipe, “MediaPipe Face Mesh,” Google AI Edge, 2020.
9. H. Fang, S. Mu, X. Chen, J. Naz, et al., “Real-time classroom behavior and drawing pattern analysis using YOLOv11 for multi-class student activity detection,” Discover Computing, vol. 29, 2026.
10. Y. Li and Q. Chen, “A lightweight YOLOv11n-based framework for student behavior detection in smart classroom environments,” Discover Applied Sciences, vol. 8, article 86, 2026.
11. “Real-time Smartphone Usage Surveillance System Based on YOLOv5,” International Journal on Informatics for Development, 2023.
12. Revathy, A. Merisha, and V. Shivani, “AI-Enabled Smart Classroom: Monitoring Mobile Phone Usage with Real-Time Alerts,” 2nd International Conference on Global Trends in Engineering and Technological Advancement, 2025.
13. Python Software Foundation, “pyttsx3: Offline Text-to-Speech Library for Python,” Python Package Index, 2023.
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