Current - Issue
Year 2026 · Volume 5 · Issue 2
Original Article
Hybrid Adaptive Face Recognition Framework (HAFRF): A Machine Learning Approach Integrating YOLOv11, FaceNet, Adaptive PCA, and Support Vector Machine
Janani Pappu PE1
Devanathan B2
1 2 Department of Computer and Information Science, Annamalai University, Chidambaram, Tamilnadu, India.
Published Online: May-August 2026
Pages: 951-957
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260502103References
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International Conference on Automatic Face & Gesture Recognition (FG), 2018.https://doi.org/10.1109/FG.2018.00020
9. J. Guo and S. Zhang, "InsightFace: 2D and 3D Face Analysis Project," IEEE International Conference on Automatic Face and Gesture
Recognition Workshops, 2019. https://github.com/deepinsight/insightface
10. M. Turk and A. Pentland, "Eigenfaces for Recognition," Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.
https://doi.org/10.1162/jocn.1991.3.1.71
11. C. Cortes and V. Vapnik, "Support-Vector Networks," Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
https://doi.org/10.1007/BF00994018
12. G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller, "Labeled Faces in the Wild: A Database for Studying Face Recognition in
Unconstrained Environments," University of Massachusetts Amherst, Technical Report 07-49, 2007.
https://vis-www.cs.umass.edu/lfw/
13. G. Jocher et al., Ultralytics YOLO11 Documentation, Ultralytics, 2024.
https://docs.ultralytics.com/models/yolo11/
14. R. Khanam and M. Hussain, "YOLO11: An Overview of the Key Architectural Enhancements," arXiv preprint, 2024.
https://arxiv.org/abs/2410.17725
15. R. Sapkota et al., "YOLO11 to Its Genesis: A Decadal and Comprehensive Review of the YOLO Series," arXiv preprint, 2024.
https://arxiv.org/abs/2406.19407
16. W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, "Face Recognition: A Literature Survey," ACM Computing Surveys, vol. 35, no. 4,
pp. 399-458, 2003.https://doi.org/10.1145/954339.954342
17. S. Balaban, "Deep Learning and Face Recognition: The State of the Art," arXiv preprint, 2019.
https://arxiv.org/abs/1902.03524.
Integrated Multi-Level CNN," Multimedia Tools and Applications, vol. 84, pp. 12715–12737, 2025. doi: 10.1007/s11042-024-19521-0.
https://doi.org/10.1007/s11042-024-19521-0
2. N. El Fadel, "Facial Recognition Algorithms: A Systematic Literature Review," Journal of Imaging, vol. 11, no. 2, Art. 58, 2025. doi:
10.3390/jimaging11020058.https://doi.org/10.3390/jimaging11020058
3. A. Nemavhola, C. Chibaya, and S. Viriri, "A Systematic Review of CNN Architectures, Databases, Performance Metrics, and Applications
in Face Recognition," Information, vol. 16, no. 2, Art. 107, 2025. doi: 10.3390/info16020107.
https://doi.org/10.3390/info16020107
4. A. Nemavhola, S. Viriri, and C. Chibaya, "A Scoping Review of Literature on Deep Learning Techniques for Face Recognition," Human
Behavior and Emerging Technologies, vol. 2025, Art. ID 5979728, 2025. doi: 10.1155/hbe2/5979728.
https://doi.org/10.1155/hbe2/5979728
5. F. Schroff, D. Kalenichenko, and J. Philbin, "FaceNet: A Unified Embedding for Face Recognition and Clustering," Proc. IEEE Conference
on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 815-823.
https://doi.org/10.1109/CVPR.2015.72986826. J. Deng, J. Guo, N. Xue, and S. Zafeiriou, "ArcFace: Additive Angular Margin Loss for Deep Face Recognition," Proc. IEEE/CVF Conference
on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 4690-4699. https://doi.org/10.1109/CVPR.2019.00482
7. S. Chen, Y. Liu, X. Gao, and Z. Han, "MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices,"
Lecture Notes in Computer Science, vol. 10996, pp. 428-438, 2018. https://doi.org/10.1007/978-3-030-02257-3_46
8. Q. Cao, L. Shen, W. Xie, O. M. Parkhi, and A. Zisserman, "VGGFace2: A Dataset for Recognising Faces Across Pose and Age," Proc. IEEE
International Conference on Automatic Face & Gesture Recognition (FG), 2018.https://doi.org/10.1109/FG.2018.00020
9. J. Guo and S. Zhang, "InsightFace: 2D and 3D Face Analysis Project," IEEE International Conference on Automatic Face and Gesture
Recognition Workshops, 2019. https://github.com/deepinsight/insightface
10. M. Turk and A. Pentland, "Eigenfaces for Recognition," Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.
https://doi.org/10.1162/jocn.1991.3.1.71
11. C. Cortes and V. Vapnik, "Support-Vector Networks," Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
https://doi.org/10.1007/BF00994018
12. G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller, "Labeled Faces in the Wild: A Database for Studying Face Recognition in
Unconstrained Environments," University of Massachusetts Amherst, Technical Report 07-49, 2007.
https://vis-www.cs.umass.edu/lfw/
13. G. Jocher et al., Ultralytics YOLO11 Documentation, Ultralytics, 2024.
https://docs.ultralytics.com/models/yolo11/
14. R. Khanam and M. Hussain, "YOLO11: An Overview of the Key Architectural Enhancements," arXiv preprint, 2024.
https://arxiv.org/abs/2410.17725
15. R. Sapkota et al., "YOLO11 to Its Genesis: A Decadal and Comprehensive Review of the YOLO Series," arXiv preprint, 2024.
https://arxiv.org/abs/2406.19407
16. W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, "Face Recognition: A Literature Survey," ACM Computing Surveys, vol. 35, no. 4,
pp. 399-458, 2003.https://doi.org/10.1145/954339.954342
17. S. Balaban, "Deep Learning and Face Recognition: The State of the Art," arXiv preprint, 2019.
https://arxiv.org/abs/1902.03524.
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