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Original Article
Smart Attendance System Using Face Recognition and Gaze-Based Attention Monitoring
Abubakkar Sithik M1
Gowrish N2
Kiran C3
Chaitra4
Abhishek5
1 Assistant Professor, Department of CSE, Raja Rajeswari college of Engineering, Bengaluru, Karnataka, India. 2 3 4 5 Department of CSE, Raja Rajeswari college of Engineering, Bengaluru, Karnataka, India.
Published Online: January-February 2026
Pages: 65-75
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260701008References
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2020.
2. S. Patel and R. Shah, “Smart Classroom Monitoring Using Computer Vision,” IEEE Access, vol. 9, pp. 112345–
112356, 2021.
3. J. Lee, H. Kim, and S. Park, “Vision-Based Gaze Tracking for Attention Analysis,” IEEE Transactions on Human-
Machine Systems, vol. 49, no. 4, pp. 345–356, 2019.
4. M. Turk and A. Pentland, “Eigenfaces for Recognition,” Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71–86,
1991.
5. P. Viola and M. Jones, “Rapid Object Detection Using a Boosted Cascade of Simple Features,” in Proc. IEEE
CVPR, 2001, pp. 511–518.
6. S. Zafeiriou, C. Zhang, and Z. Zhang, “A Survey on Face Detec-tion in the Wild,” Computer Vision and Image
Understanding, vol. 138, pp. 1–24, 2015.
7. T. Baltrusaitis, P. Robinson, and L.-P. Morency, “OpenFace: An Open Source Facial Behavior Analysis Toolkit,”
in Proc. IEEE WACV, 2016.
8. K. Zhang, Z. Zhang, Z. Li, and Y. Qiao, “Joint Face Detec-tion and Alignment Using Multi-Task Cascaded CNNs,”
IEEE Signal Processing Letters, vol. 23, no. 10, pp. 1499–1503, 2016.
9. A. George and A. Routray, “A Real-Time Eye Gaze Direction Classification Using Webcam,” in Proc. IEEE
International Conference on Signal Processing and Communications, 2016.
10. Y. Wang and M. Sung, “Driver Gaze Zone Estimation Using Convolutional Neural Networks,” in Proc. IEEE ICIP,
2016.
11. A. K. Jain, A. Ross, and S. Prabhakar, “An Introduction to Biometric Recognition,” IEEE Transactions on
Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 4–20, 2004.
12. S. Marcel and S. Li, “Biometric Face Recognition: From Algo-rithms to Applications,” IEEE Signal Processing
Magazine, vol. 31, no. 3, pp. 56–63, 2014.
13. Google Research, “MediaPipe Face Mesh: Real-Time Face Landmark Detection,” 2020. [Online].
Available: https://google.github.io/mediapipe/
14. OpenCV Team, “OpenCV: Open Source Computer Vision Library,” 2022. [Online]. Available: https://opencv.org
15. Twilio Inc., “Programmable SMS API Documentation,” 2022. [Online]. Available: https://www.twilio.com/docs/sms
16. R. Girshick et al., “Rich Feature Hierarchies for Accurate Object Detection,” IEEE CVPR, 2014.
17. T. Hassner et al., “Effective Face Frontalization in Uncon-strained Images,” IEEE CVPR, 2015.
18. A. Krizhevsky et al., “ImageNet Classification with Deep CNNs,” Neural Information Processing Systems, 2012.
19. Z. Zhang, “A Flexible New Technique for Camera Calibration,” IEEE TPAMI, vol. 22, no. 11, 2000.
20. Y. Sun et al., “Deep Learning Face Representation by Joint Identification-Verification,” IEEE NIPS, 2014.
21. IEEE Standards Association, “Ethically Aligned Design for AI Systems,” IEEE, 2019.
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