Advancements and Challenges in Face Recognition Systems : A Deep Learning Approach for Secure and Ethical Deployment

Authors

  • Alok Mihsra  Assistant Professor, Department of Computer Science & Engineering, Ambalika Institute of Management & Technology, Lucknow, Uttar Pradesh, India
  • Vipin Rawat  Assistant Professor, Department of Computer Science & Engineering, Ambalika Institute of Management & Technology, Lucknow, Uttar Pradesh, India
  • Atebar Haider  Assistant Professor, Department of Computer Science & Engineering, Ambalika Institute of Management & Technology, Lucknow, Uttar Pradesh, India
  • Niraj Kumar Singh  Assistant Professor, Department of Computer Science & Engineering, Ambalika Institute of Management & Technology, Lucknow, Uttar Pradesh, India
  • Dr. Razia Sultan  Associate Professor, Department of Computer Science & Engineering, Ambalika Institute of Management & Technology, Lucknow, Uttar Pradesh, India
  • M. B. Singh  Assistant Professor, Department of Computer Science & Engineering, Ambalika Institute of Management & Technology, Lucknow, Uttar Pradesh, India

Keywords:

Face Recognition, Deep Learning, Convolutional Neural Networks (CNNs), Feature Extraction, Ethical Implications

Abstract

This paper presents an overview of face recognition systems, highlighting their key components and advancements. Utilizing deep learning techniques, such as convolutional neural networks (CNNs), these systems process and extract unique facial features for identification or verification. The paper covers core processes like preprocessing, feature extraction, and recognition, while addressing challenges such as face spoofing, privacy concerns, and ethical implications. Applications in security, surveillance, and personalized access are discussed, emphasizing the importance of scalability, accuracy, and responsible deployment. The model's modular and adaptable structure demonstrates its relevance and potential in various real-world scenarios.

References

  1. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. *Advances in Neural Information Processing Systems*, 25.
  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. Gross, R., Matthews, I., Cohn, J., Kanade, T., & Baker, S. (2010). Multi-PIE. *Image and Vision Computing*, 28(5), 807-813.
  4. Kazemi, V., & Sullivan, J. (2014). One millisecond face alignment with an ensemble of regression trees. *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*.
  5. Taigman, Y., Yang, M., Ranzato, M. A., & Wolf, L. (2014). DeepFace: Closing the gap to human-level performance in face verification. *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*.
  6. Chakka, M. M., Anjos, A., Marcel, S., & Krichen, E. (2011). Competition on countermeasures to 2D facial spoofing attacks. *2011 IEEE International Joint Conference on Biometrics*.
  7. Hampapur, A., et al. (2005). Smart video surveillance: Exploring the concept of multiscale spatiotemporal tracking. *Signal Processing Magazine, IEEE*, 22(2), 38-51.
  8. Goodfellow, I., et al. (2014). Generative adversarial networks. *Advances in Neural Information Processing Systems*, 27.
  9. Scheirer, W. J., Jain, L. P., & Boult, T. E. (2014). Probability models for open set recognition. *IEEE Transactions on Pattern Analysis and Machine Intelligence*, 36(11), 2317-2324.
  10. Zhao, W., Chellappa, R., Phillips, P. J., & Rosenfeld, A. (2003). Face recognition: A literature survey. *ACM Computing Surveys (CSUR)*, 35(4), 399-458. 
  11. Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. *Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition*. 
  12. Turk, M., & Pentland, A. (1991). Eigenfaces for recognition. *Journal of Cognitive Neuroscience*, 3(1), 71-86. 
  13. Li, S. Z., & Jain, A. K. (2011). *Handbook of Face Recognition*. Springer. 
  14. Jain, A. K., Ross, A., & Pankanti, S. (2006). Biometrics: A tool for information security. *IEEE Transactions on Information Forensics and Security*, 1(2), 125-143. 
  15. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. *Advances in Neural Information Processing Systems*, 25. 
  16. 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*. 
  17. Taigman, Y., Yang, M., Ranzato, M. A., & Wolf, L. (2014). DeepFace: Closing the gap to human-level performance in face verification. *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*. 
  18. Gross, R., Matthews, I., Cohn, J., Kanade, T., & Baker, S. (2010). Multi-PIE. *Image and Vision Computing*, 28(5), 807-813. 
  19. Goodfellow, I., et al. (2014). Generative adversarial networks. *Advances in Neural Information Processing Systems*, 27. 

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2024-02-12

Issue

Volume 10, Issue 1, January-February-2024

Section

Research Articles

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How to Cite

[1]
Alok Mihsra, Vipin Rawat, Atebar Haider, Niraj Kumar Singh, Dr. Razia Sultan, M. B. Singh, " Advancements and Challenges in Face Recognition Systems : A Deep Learning Approach for Secure and Ethical Deployment" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 10, Issue 1, pp.294-302, January-February-2024.