JEE_V7_N1_RP2 Hatem Elaydi Mohammed Alhanjouri Mohanad A. M. Abukmeil Journal on Electrical Engineering 2230 – 7176 7 1 9 19 Palmprint Identification, 2-D Discrete Wavelet, Ridgelets, Curvelets, Contourlets, 2-DPCA, Back Propagation Neural Network 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. July - September 2013 Copyright © 2013 i-manager publications. All rights reserved. i-manager Publications http://www.imanagerpublications.com/Article.aspx?ArticleId=2426