Journal of Computer Sciences and Applications
ISSN (Print): 2328-7268 ISSN (Online): 2328-725X Website: http://www.sciepub.com/journal/jcsa Editor-in-chief: Minhua Ma, Patricia Goncalves
Open Access
Journal Browser
Go
Journal of Computer Sciences and Applications. 2015, 3(2), 23-28
DOI: 10.12691/jcsa-3-2-1
Open AccessArticle

Palmprint Recognitionvia Bandlet, Ridgelet, Wavelet and Neural Network

Mohanad A. M. Abukmeil1, , Hatem Elaydi1, and Mohammed Alhanjouri2

1Electrical Engineering, Islamic University of Gaza, Gaza, Palestine

2Computer Engineering, Islamic University of Gaza, Gaza, Palestine

Pub. Date: March 28, 2015

Cite this paper:
Mohanad A. M. Abukmeil, Hatem Elaydi and Mohammed Alhanjouri. Palmprint Recognitionvia Bandlet, Ridgelet, Wavelet and Neural Network. Journal of Computer Sciences and Applications. 2015; 3(2):23-28. doi: 10.12691/jcsa-3-2-1

Abstract

Palmprint recognition has emerged as a valid biometric based personal identification tool. Palmprints with high resolution features such minutia points, ridges and singular points or low resolution features such as wrinkles and principals determine their applications. In this paper a 700nm spectral band PolyU hyperspectral palmprint database is utilized and the multiscale band let image transform is utilized in features extraction; moreover, its results are compared with the ridgelet and 2D discrete wavelet results. The size of features is reduced using principle component analysis and linear discriminate analysis; in addition, a feed forward back-propagation neural network is used as a classifier. The results show that the recognition rate accuracy of the band let transform outperforms others.

Keywords:
palmprint identification 2D discrete wavelet ridgelet bandlet neural network

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]  D. Zhang, Wai-Kin Kong, J. You and Michael Wong, “Online palmprint identification,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, pp. 1041-1050, Sept. 2003.
 
[2]  A. Jain, R. Bolle and S. Pankanti (eds.), Biometrics: Personal Identification in Networked Society, Boston, Mass: Kluwer Academic Publishers, 1999.
 
[3]  A. Kong, D. Zhang and M. Kamel, “A survey of palmprint recognition,” Journal of Pattern Recognition, vol. 42, pp. 1408-1418, July. 2009.
 
[4]  Jiwen Lu, Erhu Zhang, Xiaobin Kang and YanxueXue, “Palmprint recognition using wavelet decomposition and 2D principal component analysis,” International conference on Communications, Circuits and Systems Proceedings, vol. 3, pp. 2133-2136, June. 2006.
 
[5]  H.Masood, M. Asim, M. Mumtaz and A. Mansoor, “Combined contourlet and non-subsampled contourlet transforms based approach for personal identification using palmprint,” Digital Image Computing: Techniques and Applications, DICTA '09, pp.408-415, Dec.2009.
 
[6]  M. Sharkas, I. El-Rube and M.A. Mostafa, “The contourlet transform with the principal component analysis for palmprint recognition,” International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN), pp. 262-267, July. 2010.
 
[7]  H.B.Kekre, R. Vig and S. Bisani, “Identification of multi-spectral palmprints using energy compaction by hybrid wavelet,” International Conference on Biometric (ICB), pp. 433-438, March. 2012.
 
[8]  H. Elaydi, M. Alhanjouri, and M. Abukmeil, “Palmprint recognition using 2-d wavelet, ridgelet, curvelet and contourlet,” i-manager's Journal on Electrical Engineering (JEE), vol. 7 Issue 1, pp. 9-19, Jul-Sep 2013.
 
[9]  Hatem Elaydi, Mohanad A. M. Abukmeil, Mohammed Alhanjouri, Palmprint Recognition Using Multiscale Transform, Linear Discriminate Analysis, and Neural Network, Science Journal of Circuits, Systems and Signal Processing. vol. 2, no. 5, pp. 112-118, 2013.
 
[10]  J. Andrew Bangham and Richard V. Aldridge, “Multiscale decomposition using median and morphological filters,” IEEE Winter Workshop on Nonlinear Digital Signal Processing, 6.1_1.1 - 6.1_1.4, 1993.
 
[11]  Alexandru Isar, Sorin Moga, and Xavier Lurton, “A statistical analysis of the 2d discrete wavelet transform,” Proceedings of the International Conference AMSDA 2005, 1275-1281, 17-20 May 2005
 
[12]  Samuel Foucher, Goz´e Bertin B´eni´e, Jean-Marc Boucher, “Multiscale map filtering of sar images,” IEEE Transactions on Image Processing, vol. 10, no.1, January 2001, 49-60.
 
[13]  E. J. Candµes and D. L. Donoho, “Ridgelets: a key to higher- dimensional intermittency?” Phil. Trans. R. Soc. Lond. A., pp. 2495-2509, 1999.
 
[14]  G. T. Herman, Image Reconstruction from Projections: The Fundamentals of Computerized Tomography, Academic Press, 1980.
 
[15]  A. Rosenfeld and A. C. Kak, Digital Picture Processing, Aca- demic Press, 2nd edition, 1982..
 
[16]  H. Führ, L. Demaret and F. Friedrich, Beyond wavelets: new image representation paradigms. Book chapter. In: M. Barni and F. Bartolini (Eds.), Document and Image Compression, CRC Press, Boca Raton, FL, 2006.
 
[17]  Stéphane Mallat and Gabriel Peyré, “A Review of Bandlet Methods for Geometrical Image Representation,” Numerical Algorithms 44, 3 (2007) 205-234,February 26, 2008.
 
[18]  Gabriel Peyréa, Erwan Le Pennecb, Charles Dossalc, Stéphane Mallatd,” Geometrical Image Estimation with Orthogonal Bandlet Bases,”Numerical Algorithms 44, 3 (2007) 205-234”.
 
[19]  B. Alpert,” Wavelets and Other Bases for Fast Numerical Linear Algebra,” pp. 181–216. C. K. Chui, editor, Academic Press, San Diego, CA, USA, 1992.
 
[20]  J. Yangand D. Zhang, “Two-dimensional PCA: A new approach to appearance-based face representation and recognition,” IEEE Trans. Pattern Anal. Machine Intell. PAMI-26 (1), 131-137, 2004.
 
[21]  W.S. Zheng, J.H. Lai, S.Z. Li, “1D-LDA vs. 2DLDA: When is vector-based linear discriminate analysis better than matrix-based?” Pattern Recognition, vol. 41, pp. 2156-2172, July 2008.
 
[22]  P.Latha, L.Ganesan and S.Annadurai, “Face recognition using neural networks,” Signal Processing: An International Journal (SPIJ) 3 (5), 153-160, Nov 2009.
 
[23]  S.Lawrence, C.L.Giles, A.C.Tsoi, and A.d.Back, “Face recognition: a convolutional neural network approach,” IEEE Transactions of Neural Networks, vol.8, no.1, pp.98-113, 1993.
 
[24]  Department of Computing, The Hong Kong Polytechnic University (PolyU), Hyperspectra Palmprint database, Polyu, accessed on Aug. 22, 2013, available at: http://www4.comp.polyu.edu.hk/~biometrics/Hyperspectral Palmprint/HSP.htm.
 
[25]  Xiaobo Qu, Bandelet Image Fusion Toolbox, accessed on 27 March 27, 2014. Available at: http://www.quxiaobo.org/software/software_BandeletFusion.html.