A Data-Driven Approach for Detecting Attacks in Finger Vein Recognition Using Deep Neural Networks

  • Akhil S Krishnan Bachelor of Technology, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, India
  • Ramanathan N Bachelor of Technology, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, India
  • Anirudh M Bachelor of Technology, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, India
  • T. Rajesh [email protected]
Keywords: Finger Vein Detection, Deformed Finger Images, Convolutional Neural Network, VGG16 Jupyter Notebook, Python, Issues Caused by Deformed Finger, Deformed Finger Posture

Abstract

Finger vein recognition offers precision, quickness, sanitation, administration, and privacy as a non-contact biometric approach. Inconsistency between snaps from the same finger reduces software efficacy due to posture variations during finger image capture. Finger posture can also cause ID errors. Finger deformations can be used to determine a genuine or impostor match in finger biometrics. In our study, we use five types of finger deformations and a neural network to determine if an input image of a finger with finger vein data is a match or an attack attempt. Deformed finger vein imagery improves the biometric system’s attack detection. Experimental results show that the suggested algorithmic approach outperforms the standard technique in matching and efficiency.

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Published
2022-12-16
How to Cite
Krishnan, A. S., N, R., M , A., & Rajesh , T. (2022). A Data-Driven Approach for Detecting Attacks in Finger Vein Recognition Using Deep Neural Networks. Central Asian Journal of Medical and Natural Science, 3(6), 435-462. https://doi.org/10.17605/cajmns.v3i6.1231
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Articles