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Palmprint Recognition Using Multiscale Transform, Linear Discriminate Analysis, and Neural Network

Received: 21 October 2013     Published: 10 November 2013
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Abstract

Palmprint recognition is gaining grounds as a biometric system for forensic and commercial applications. Palmprint recognition addressed the recognition issue using low and high resolution images. This paper uses PolyU hyperspectral palmprint database, and applies back-propagation neural network for recognition, linear discriminate analysis for dimensionality reduction, and 2D discrete wavelet, ridgelet, curvelet, and contourlet for feature extraction. The recognition rate accuracy shows that contourlet outperforms other transforms.

Published in Science Journal of Circuits, Systems and Signal Processing (Volume 2, Issue 5)
DOI 10.11648/j.cssp.20130205.13
Page(s) 112-118
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2013. Published by Science Publishing Group

Keywords

2D Discrete Wavelet, Ridgelet, Curvelet, Contourlet, Linear Discriminate Analysis, Neural Network

References
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Cite This Article
  • APA Style

    Hatem Elaydi, Mohanad A. M. Abukmeil, Mohammed Alhanjouri. (2013). Palmprint Recognition Using Multiscale Transform, Linear Discriminate Analysis, and Neural Network. Science Journal of Circuits, Systems and Signal Processing, 2(5), 112-118. https://doi.org/10.11648/j.cssp.20130205.13

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    ACS Style

    Hatem Elaydi; Mohanad A. M. Abukmeil; Mohammed Alhanjouri. Palmprint Recognition Using Multiscale Transform, Linear Discriminate Analysis, and Neural Network. Sci. J. Circuits Syst. Signal Process. 2013, 2(5), 112-118. doi: 10.11648/j.cssp.20130205.13

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    AMA Style

    Hatem Elaydi, Mohanad A. M. Abukmeil, Mohammed Alhanjouri. Palmprint Recognition Using Multiscale Transform, Linear Discriminate Analysis, and Neural Network. Sci J Circuits Syst Signal Process. 2013;2(5):112-118. doi: 10.11648/j.cssp.20130205.13

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  • @article{10.11648/j.cssp.20130205.13,
      author = {Hatem Elaydi and Mohanad A. M. Abukmeil and Mohammed Alhanjouri},
      title = {Palmprint Recognition Using Multiscale Transform, Linear Discriminate Analysis, and Neural Network},
      journal = {Science Journal of Circuits, Systems and Signal Processing},
      volume = {2},
      number = {5},
      pages = {112-118},
      doi = {10.11648/j.cssp.20130205.13},
      url = {https://doi.org/10.11648/j.cssp.20130205.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cssp.20130205.13},
      abstract = {Palmprint recognition is gaining grounds as a biometric system for forensic and commercial applications. Palmprint recognition addressed the recognition issue using low and high resolution images. This paper uses PolyU hyperspectral palmprint database, and applies back-propagation neural network for recognition, linear discriminate analysis for dimensionality reduction, and 2D discrete wavelet, ridgelet, curvelet, and contourlet for feature extraction. The recognition rate accuracy shows that contourlet outperforms other transforms.},
     year = {2013}
    }
    

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  • TY  - JOUR
    T1  - Palmprint Recognition Using Multiscale Transform, Linear Discriminate Analysis, and Neural Network
    AU  - Hatem Elaydi
    AU  - Mohanad A. M. Abukmeil
    AU  - Mohammed Alhanjouri
    Y1  - 2013/11/10
    PY  - 2013
    N1  - https://doi.org/10.11648/j.cssp.20130205.13
    DO  - 10.11648/j.cssp.20130205.13
    T2  - Science Journal of Circuits, Systems and Signal Processing
    JF  - Science Journal of Circuits, Systems and Signal Processing
    JO  - Science Journal of Circuits, Systems and Signal Processing
    SP  - 112
    EP  - 118
    PB  - Science Publishing Group
    SN  - 2326-9073
    UR  - https://doi.org/10.11648/j.cssp.20130205.13
    AB  - Palmprint recognition is gaining grounds as a biometric system for forensic and commercial applications. Palmprint recognition addressed the recognition issue using low and high resolution images. This paper uses PolyU hyperspectral palmprint database, and applies back-propagation neural network for recognition, linear discriminate analysis for dimensionality reduction, and 2D discrete wavelet, ridgelet, curvelet, and contourlet for feature extraction. The recognition rate accuracy shows that contourlet outperforms other transforms.
    VL  - 2
    IS  - 5
    ER  - 

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Author Information
  • Electrical Engineering, the Islamic University, Gaza, Palestine

  • Electrical Engineering, the Islamic University, Gaza, Palestine

  • Computer Engineering, the Islamic University, Gaza, Palestine

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