Vol. 2 No. 1 (2025), Articles
Vol. 2 No. 1 (2025)

FACE IMAGE RECOGNITION METHODS AND THEIR OPTIMIZATION IN BIOMETRIC IDENTIFICATION SYSTEMS WITHIN EDUCATIONAL SYSTEMS

Articles
Published 2025-06-09
Ortikova Feruza Sodiq kizi.
Tashkent University of Information Technologies named after Muhammad al-Khwarizmi
PDF

Keywords

Face image recognition, biometric identification, educational systems, deep learning, optimization, privacy, preprocessing, real-time performance.

Abstract

Face image recognition (FIR) has emerged as a critical component in biometric identification systems, particularly in the context of educational environments. This paper provides an in-depth analysis of state-of-the-art FIR methods and their optimization for deployment in schools and universities. We explore various algorithms, including deep learning-based approaches, preprocessing techniques, and system integration strategies to enhance accuracy and efficiency. The study also discusses the challenges of ethical considerations, privacy concerns, and implementation costs. Experimental results demonstrate the feasibility and reliability of optimized FIR systems in educational settings, offering a foundation for further research and development.
PDF

References

1. Taigman, Y., Yang, M., Ranzato, M. A., & Wolf, L. (2014). DeepFace: Closing the Gap to Human-Level Performance in Face Verification. IEEE Conference on Computer Vision and Pattern Recognition.

2. Schroff, F., Kalenichenko, D., & Philbin, J. (2015). FaceNet: A Unified Embedding for Face Recognition and Clustering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

3. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

4. Parkhi, O. M., Vedaldi, A., & Zisserman, A. (2015). Deep Face Recognition. British Machine Vision Conference.

5. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

6. Zhang, Z., Luo, P., Loy, C. C., & Tang, X. (2016). Facial Landmark Detection by Deep Multi-Task Learning. European Conference on Computer Vision.

7. Sun, Y., Liang, D., Wang, X., & Tang, X. (2015). DeepID3: Face Recognition with Very Deep Neural Networks. arXiv preprint arXiv:1502.00873.

8. Wang, M., Deng, W. (2021). Deep Face Recognition: A Survey. Neurocomputing.

9. Masip, D., & Bagà, J. (2020). Privacy-Preserving Techniques in Face Recognition Systems. IEEE Access.

10. Deng, J., Guo, J., Xue, N., & Zafeiriou, S. (2019). ArcFace: Additive Angular Margin Loss for Deep Face Recognition. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.