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Contactless Palmprint Recognition Using Binarized Statistical Image Features-Based Multiresolution Analysis.

Nadia Amrouni1, Amir Benzaoui2, Rafik Bouaouina3

  • 1LIST Laboratory, University of M'Hamed Bougara Boumerdes, Avenue of Independence, Boumerdes 35000, Algeria.

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|December 23, 2022
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Summary
This summary is machine-generated.

This study introduces a new contactless palmprint recognition method using multiresolution analysis for feature extraction. The approach achieves high accuracy, demonstrating its effectiveness for personal identification.

Keywords:
binarized statistical image featuresbiometricsmultiresolution analysispalmprint recognitiontexture descriptorswavelet analysis

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Area of Science:

  • Biometrics
  • Computer Vision
  • Pattern Recognition

Background:

  • Palmprint recognition is a reliable biometric identification method.
  • System performance relies heavily on effective feature extraction.
  • Contactless palmprint recognition presents unique challenges.

Purpose of the Study:

  • To propose a novel three-step approach for contactless palmprint recognition.
  • To enhance feature extraction using multiresolution analysis.
  • To improve the accuracy of personal identification systems.

Main Methods:

  • Pre-processing using median filtering and CLAHE for noise reduction and lighting equalization.
  • Multiresolution analysis to extract Binarized Statistical Image Features (BSIF) at various Discrete Wavelet Transform (DWT) resolutions.
  • Classification using a K-nearest neighbors (K-NN) classifier.

Main Results:

  • Achieved Rank-1 recognition rates of 98.77% on the IITD database.
  • Achieved Rank-1 recognition rates of 98.10% on the CASIA database.
  • Demonstrated superior performance compared to existing state-of-the-art methods.

Conclusions:

  • The proposed multiresolution analysis for feature extraction is effective for contactless palmprint recognition.
  • The three-step approach offers a robust solution for personal identification.
  • The method shows significant potential for real-world biometric applications.