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Original Article

Face Attendance System Using Machine Learning

  • Paper ID 227
  • Volume 5, Issue 5
  • Pages 19-23
  • Published Online: September-October 2025
  • 26 Views
Open Access Peer Reviewed Crossref DOI

Authors

  • Mohd. Irfan1
  • Abdul Rahman2

1 Student, MCA, Deccan College of Engineering and Technology, Hyderabed, Telangana, India. 2 Assistant Professor, MCA, Deccan College of Engineering and Technology, Hyderabed, Telangana, India.

Article Information

Published Online
September-October 2025
Pages
19-23
Article Type
Original Article
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References

1. A. Geitgey, face_recognition: Python facial recognition API. GitHub.
2. G. Bradski, “The OpenCV Library,” Dr. Dobb’s Journal of Software Tools, 2000.
3. F. Pedregosa, G. Varoquaux, A. Gramfort, et al., “Scikit-learn: Machine Learning in Python,” JMLR, vol. 12, pp. 2825–2830, 2011.
4. D. E. King, “Dlib-ml: A Machine Learning Toolkit,” JMLR, vol. 10, pp. 1755–1758, 2009.
5. FastAPI Documentation, https://fastapi.tiangolo.com.
6. O. M. Parkhi, A. Vedaldi, A. Zisserman, “Deep Face Recognition,” BMVC, 2015.
7. Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, “DeepFace: Closing the Gap to Human-Level Performance,” CVPR, 2014.
8. K. Zhang, Z. Zhang, Z. Li, and Y. Qiao, “Joint Face Detection and Alignment using Multi-task Cascaded CNNs,” IEEE SPL, 2016.
9. R. Szeliski, Computer Vision: Algorithms and Applications, Springer, 2010.
10. P. Viola and M. Jones, “Rapid Object Detection using a Boosted Cascade of Simple Features,” CVPR, 2001.
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