Face Recognition Using Transfer Learning

  • Unique Paper ID: 152422
  • Volume: 8
  • Issue: 3
  • PageNo: 340-347
  • Abstract:
  • Convolutional neural networks (CNNs) are wont to achieve unprecedented facial accuracy on large labeled databases. However, smaller organizations wishing to provide secure biometric-based access to its members may find it challenging to train CNNs with minimal computer resources and small training databases. We address this issue by proposing a facial recognition approach and validation based on transfer learning that requires minimal retraining. We show that by adding a single layer of a single trained element with a small number of neurons in a pre-trained network to be able to function and validate authentication can be achieved in small data sets. In addition, new courses can be registered with high monitoring accuracy without properly adjusting the new feature layer.

Copyright & License

Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{152422,
        author = {Yati Saxena and Vaibhav Kumar and Mayuri.H. Molawade},
        title = {Face Recognition Using Transfer Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {3},
        pages = {340-347},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=152422},
        abstract = {Convolutional neural networks (CNNs) are wont to achieve unprecedented facial accuracy on large labeled databases. However, smaller organizations wishing to provide secure biometric-based access to its members may find it challenging to train CNNs with minimal computer resources and small training databases. We address this issue by proposing a facial recognition approach and validation based on transfer learning that requires minimal retraining. We show that by adding a single layer of a single trained element with a small number of neurons in a pre-trained network to be able to function and validate authentication can be achieved in small data sets. In addition, new courses can be registered with high monitoring accuracy without properly adjusting the new feature layer.},
        keywords = {Face recognition, transfer learning, vgg16},
        month = {},
        }

Cite This Article

  • ISSN: 2349-6002
  • Volume: 8
  • Issue: 3
  • PageNo: 340-347

Face Recognition Using Transfer Learning

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