Palmprint Recognition Using 2-D Wavelet, Ridgelet, Curvelet and Contourlet

Hatem Elaydi*, Mohammed Alhanjouri**, Mohanad A. M. Abukmeil***
* Associate Professor, Department of Electrical Engineering, Islamic University-Gaza, Palestine.
** Associate Professor of AI & DSP, Director of Excellence and E-learning Center / Academic Affairs,IUG.
*** ICT System Engineer, Department of Electrical Engineering-Communication Field, Islamic University-Gaza, Palestine.
Periodicity:July - September'2013
DOI : https://doi.org/10.26634/jee.7.1.2426

Abstract

Palmprint recognition is a promising biometric field which is used for forensic and commercial applications. This paper provides a comparative palmprint recognition approach using multi-scale transforms: 2D wavelets, ridgelets, curvelets, and contourlets for feature extraction phase, 2-D Principal Component Analysis (2-D PCA) for dimensionality reduction and artificial neural network for recognition phase. Finally, a comparative analysis has been done. The algorithms have been tested using PolyU hyperspectral palmprint database. The recognition rate accuracy was very good and is listed in this order curvelets, contourlets, ridgelets, and 2D discrete wavelets where the curvelets outperformed the others.

Keywords

Palmprint Identification, 2-D Discrete Wavelet, Ridgelets, Curvelets, Contourlets, 2-DPCA, Back Propagation Neural Network

How to Cite this Article?

Elaydi, H., Alhanjouri, M., and Abukmeil, M. A. M. (2013). Palmprint Recognition Using 2-D Wavelet, Ridgelet, Curvelet And Contourlet. i-manager’s Journal on Electrical Engineering, 7(1), 9-19. https://doi.org/10.26634/jee.7.1.2426

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