Facial Image Verification

Authors

  • Ojasi Lavanya * kiit, India.
  • A Shivangi kiit, India.
  • Sanjidah Munim Mazumder kiit, India.

https://doi.org/10.22105/raise.v1i3.57

Abstract

With an emphasis on how Convolutional Neural Networks (CNNs) improve accuracy, adaptability, and efficiency over conventional techniques, this study investigates the incorporation of deep learning techniques in facial recognition. The paper highlights the deep learning process by describing procedures, including face identification, alignment, feature extraction, and recognition. CNNs' ability to derive intricate patterns from unprocessed image data is one of their main advantages; this enables reliable feature extraction and precise detection even in situations with changing illumination, attitude, and occlusion. Along with discussions of exciting future advancements meant to enhance fairness, robustness, and privacy preservation within facial recognition systems, challenges such as data bias, privacy problems, and adversarial susceptibility are highlighted.

Keywords:

Convolutional neural networks, Deep learning in facial recognition, Feature extraction, Image data processing, Privacy preservation, Future advancements

References

  1. [1] Qinjun, L., Tianwei, C., Yan, Z., Yuying, W. (2023). Facial recognition technology: A comprehensive overview. Acad. j. comput. inf. sci, 6(7), 15–26. https://doi.org/10.25236/AJCIS.2023.060703
  2. [2] Aloysius, N., & Geetha, M. (2017). A review on deep convolutional neural networks. 2017 international conference on communication and signal processing (ICCSP) (pp. 588–592). IEEE. https://doi.org/10.1109/ICCSP.2017.8286426
  3. [3] Ming, Y. (2019). Face detection based on viola-jones algorithm applying composite features. 2019 international conference on robots & intelligent system (ICRIS) (pp. 82–85). IEEE. https://doi.org/10.1109/ICRIS.2019.00029
  4. [4] Bhaidasna, Z. C., Swaminarayan, P. R., & Bhaidasna, H. Z. (2023). Enhancing Face Recognition With Deep Learning Architectures: A Comprehensive Review. International journal on recent and innovation trends in computing and communication, 11(9), 164–180. https://doi.org/10.17762/ijritcc.v11i9.8331
  5. [5] Hu, G., Yang, Y., Yi, D., Kittler, J., Christmas, W., Li, S. Z., & Hospedales, T. (2015). When face recognition meets with deep learning: an evaluation of convolutional neural networks for face recognition. Proceedings of the ieee international conference on computer vision workshops (pp. 142–150). IEEE. https://B2n.ir/gt1435
  6. [6] Sati, V., Garg, D., Choudhury, T., & Aggarwal, A. (2018). Facial recognition-application and future: a review. 2018 international conference on system modeling & advancement in research trends (smart) (pp. 231–235). IEEE. https://doi.org/10.1109/SYSMART.2018.8746942
  7. [7] Cruz, C., Sucar, L. E., & Morales, E. F. (2008). Real-time face recognition for human-robot interaction. 2008 8th ieee international conference on automatic face & gesture recognition (pp. 1–6). IEEE. https://doi.org/10.1109/AFGR.2008.4813386
  8. [8] Maas, A., & Kemp, C. (2009). One-shot learning with bayesian networks. Carnegie mellon university. https://B2n.ir/kf2616
  9. [9] O’shea, K., & Nash, R. (2015). An introduction to convolutional neural networks. https://doi.org/10.48550/arXiv.1511.08458
  10. [10] Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. ArXiv Preprint ArXiv:1409.1556. https://doi.org/10.48550/arXiv.1409.1556
  11. [11] Ma, Y., Zheng, Q., Liu, Y., Shi, B., Xue, X., Ji, W. (2016). Self-powered, one-stop, and multifunctional implantable triboelectric active sensor for real-time biomedical monitoring. Nano letters, 16(10), 6042–6051. https://doi.org/10.1021/acs.nanolett.6b01968
  12. [12] Singh, S., & Prasad, S. (2018). Techniques and challenges of face recognition: A critical review. Procedia computer science, 143, 536–543. https://doi.org/10.1016/j.procs.2018.10.427
  13. [13] Bradski, G., & Kaehler, A. (2008). Learning OpenCV: Computer vision with the OpenCV library. “ O’Reilly Media, Inc.” https://B2n.ir/jg4460
  14. [14] Saha, S. (2018). A comprehensive guide to convolutional neural networks—the ELI5 way. Towards data science, 15, 15. https://B2n.ir/ek1020
  15. [15] Kortli, Y., Jridi, M., Al Falou, A., & Atri, M. (2020). Face recognition systems: A survey. Sensors, 20(2), 342. https://doi.org/10.3390/s20020342
  16. [16] Lee, T., Singh, V. P., Cho, K. H., Lee, T., Singh, V. P., & Cho, K. H. (2021). Tensorflow and keras programming for deep learning. Deep learning for hydrometeorology and environmental science, 151–162. https://doi.org/10.1007/978-3-030-64777-3_11
  17. [17] Zhang, J., & Zhang, Y. (2021). A qualitative comparative analysis of tourism and gender equality in emerging economies. Journal of hospitality and tourism management, 46, 284–292. https://doi.org/10.1016/j.jhtm.2021.01.009
  18. [18] Wang, X. M., Smith, D. T., & Zhu, Q. (2023). A webcam-based machine learning approach for three-dimensional range of motion evaluation. PLoS one, 18(10), e0293178. https://doi.org/10.1371/journal.pone.0293178

Downloads

Published

2024-09-14

Issue

Vol. 1 No. 3 (2024): Research Annals of Industrial and Systems Engineering

Section

Articles

How to Cite

Facial Image Verification. (2024). Research Annals of Industrial and Systems Engineering, 1(3), 160-170. https://doi.org/10.22105/raise.v1i3.57

Similar Articles

1-10 of 19

You may also start an advanced similarity search for this article.