Frontiers in Artificial Intelligence (Aug 2024)

Enhanced fingerprint classification through modified PCA with SVD and invariant moments

  • Ala Balti,
  • Ala Balti,
  • Abdelaziz Hamdi,
  • Sabeur Abid,
  • Mohamed Moncef Ben Khelifa,
  • Mounir Sayadi

DOI
https://doi.org/10.3389/frai.2024.1433494
Journal volume & issue
Vol. 7

Abstract

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This research introduces a novel MOMENTS-SVD vector for fingerprint identification, combining invariant moments and SVD (Singular Value Decomposition), enhanced by a modified PCA (Principal Component Analysis). Our method extracts unique fingerprint features using SVD and invariant moments, followed by classification with Euclidean distance and neural networks. The MOMENTS-SVD vector reduces computational complexity by outperforming current models. Using the Equal Error Rate (EER) and ROC curve, a comparative study across databases (CASIA V5, FVC 2002, 2004, 2006) assesses our method against ResNet, VGG19, Neuro Fuzzy, DCT Features, and Invariant Moments, proving enhanced accuracy and robustness.

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