«Al-Farg’oniy avlodlari» («The descendants of Al-Fargani»)
Published December 10, 2023 | Version v1
Journal article Open

ANALYSIS OF FACIAL RECOGNITION ALGORITHMS IN THE PYTHON PROGRAMMING LANGUAGE

Creators

  • 1. Assistant of Ferghana branch of Tashkent university of information technologies named after Muhammad al-Khwarizmi

Description

Facial recognition technology has revolutionized the way we interact with the world around us. From unlocking smartphones to identifying individuals in security footage, facial recognition algorithms have become an integral part of our daily lives. However, with the increasing sophistication of facial recognition technology, it is crucial to critically evaluate its performance and potential implications. This article delves into the analysis of facial recognition algorithms in the Python programming language, exploring their accuracy, efficiency, and broader considerations for responsible implementation. By comparing the performance of three popular algorithms – Eigenfaces, Fisherfaces, and Local Binary Patterns – we aim to identify the algorithm that strikes the most favorable balance between accuracy and efficiency. Furthermore, we discuss the broader implications of facial recognition technology, highlighting the importance of addressing potential biases, ensuring data privacy, and safeguarding individual rights.

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References

  • Huang, G. B., Ramesh, V., Berg, T., & Learned-Miller, E. (2007). Labeled Faces in the Wild: A database for studying face recognition in unconstrained environments. Proceedings of the 8th IEEE International Conference on Automatic Face and Gesture Recognition, 1-8.
  • Zhao, Z., Yi, Z., Feng, W., & Huang, X. (2019). Comparative Analysis of Eigenfaces, Fisherfaces, and LBP for Facial Recognition. Journal of Computer and Communications, 7(12), 254.
  • Li, H., Wang, S., Li, X., & Deng, W. (2020). Improved Face Recognition Based on Multi-scale Convolutional Neural Network and Adaptive Pooling. IEEE Access, 8, 19682-19691.
  • Zhang, Z., Zhang, Y., Zhang, C., & Zhang, Z. (2021). Face Recognition Based on Improved VGGNet and Local Binary Pattern. IEEE Access, 9, 148193-148203.
  • Wang, S., Tang, Y., & Li, C. (2022). Lightweight Face Recognition Network with Attention Mechanism. IEEE Access, 10, 120922-120932.
  • Sun, Y., Wang, X., & Tang, X. (2014). Deep learning face recognition: From supervised to semi-supervised. Pattern Recognition, 47(5), 1391-1406.
  • Schroff, F., Kalkanis, D., & Philbin, J. (2015). FaceNet: A unified deep learning approach for facial recognition. arXiv preprint arXiv:1503.03832.
  • Taigman, Y., Yang, M., Ranzato, M., & Hinton, G. E. (2014). Deepface: Closing the gap to human-level performance in face verification. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1701-1708).
  • Parkhi, O. M., & Belhumeur, P. N. (2013). Eigenfaces for image retrieval. IEEE transactions on image processing, 22(10), 3861-3869.
  • Kodirov, A. (2023, November). Advanced Python Techniques: Harnessing the Power of Python for Experienced Developers. In Conference on Digital Innovation:" Modern Problems and Solutions".
  • Kodirov, A. (2023, November). Python for Beginners: A Comprehensive Guide to Programming Fundamentals. In Conference on Digital Innovation:" Modern Problems and Solutions".
  • Al-Jubouri, A. A., & Mohammed, S. A. (2022). A hybrid feature extraction approach for facial recognition using deep learning and local binary patterns. Multimedia Tools and Applications, 83(1), 261-285.
  • Hassan, M. Y., Malik, S. A., & Uddin, M. S. (2022). A Robust and Efficient Face Recognition Framework Based on Improved Local Binary Patterns and Convolutional Neural Network. IEEE Access, 10, 119157-119170.
  • Liu, S., & Liu, X. (2023). A Lightweight and Efficient Face Recognition Network Based on Improved Capsule Networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(1), 1-10.
  • Sahu, S., & Gupta, D. (2023). A Novel Face Recognition Technique Using Optimized Local Binary Pattern and Deep Convolutional Neural Networks. IEEE Robotics and Automation Letters, 8(1), 352-357.
  • Wang, Z., Yang, D., & Chen, Y. (2023). A Novel Two-Stage Face Recognition Method Based on Enhanced Local Binary Patterns and Attention Mechanism. Journal of Computer and Communications, 11(3), 98.