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Transfer Learning based CNN Model for Personal Authentication using Finger Vein Biometric

  • Unique Paper ID: 153764
  • Volume: 8
  • Issue: 8
  • PageNo: 550-555
  • Abstract:
  • In this paper, the performance of CNN (convolutional neural networks) such as Alex net, Squeeze net and Google Net (Inception) are analyzed for the finger vein based personal authentication with respect to control, access to the confidential data. The finger vein images from SDUMLA HMT database are used for this research work. Using a wiener filter the noises are removed from the finger vein images. The noise free images are provided for training to Alex net, Squeeze net and Google net network to recognize persons for finger vein authentication. The finger vein authentication using the three pre-trained networks includes loading finger vein images dataset, loading pretrain network, training network through transfer learning, image classification and image validation. The experiment exhibits the outstanding performance of Google net over the Alex net and Squeeze net on several parameters including computation time, the initial learning, accuracy, number of layers and dropout.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 8
  • Issue: 8
  • PageNo: 550-555

Transfer Learning based CNN Model for Personal Authentication using Finger Vein Biometric

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Impact Factor
8.01 (Year 2024)
UGC Approved
Journal no 47859

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