CFP last date
20 December 2024
Reseach Article

A Comprehensive Study of Palmprint based Authentication

by Madasu Hanmandlu, Neha Mittal, Ankit Gureja, Ritu Vijay
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 37 - Number 2
Year of Publication: 2012
Authors: Madasu Hanmandlu, Neha Mittal, Ankit Gureja, Ritu Vijay
10.5120/4580-6499

Madasu Hanmandlu, Neha Mittal, Ankit Gureja, Ritu Vijay . A Comprehensive Study of Palmprint based Authentication. International Journal of Computer Applications. 37, 2 ( January 2012), 17-24. DOI=10.5120/4580-6499

@article{ 10.5120/4580-6499,
author = { Madasu Hanmandlu, Neha Mittal, Ankit Gureja, Ritu Vijay },
title = { A Comprehensive Study of Palmprint based Authentication },
journal = { International Journal of Computer Applications },
issue_date = { January 2012 },
volume = { 37 },
number = { 2 },
month = { January },
year = { 2012 },
issn = { 0975-8887 },
pages = { 17-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume37/number2/4580-6499/ },
doi = { 10.5120/4580-6499 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:23:15.693246+05:30
%A Madasu Hanmandlu
%A Neha Mittal
%A Ankit Gureja
%A Ritu Vijay
%T A Comprehensive Study of Palmprint based Authentication
%J International Journal of Computer Applications
%@ 0975-8887
%V 37
%N 2
%P 17-24
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents some new features for the palmprint based authentication. The Region of interest (ROI) is extracted from the palmprint image by finding a tangent to the curves between fingers. The perpendicular bisector of this tangent and the tangent itself help demarcate the rectangular area that forms the ROI of the palmprint. Four approaches are presented for the feature extraction. In the first approach the ROI is divided into a suitable number of non-overlapping windows from which fuzzy features are extracted. In the second approach multi-scale wavelet decomposition is applied on the ROI and the detail images are combined to yield a composite image which is partitioned into non-overlapping windows and energy features are extracted. In the third approach sigmoid features are extracted from the ROI and in the fourth approach feature extraction is done using Local Binary Pattern (LBP) based on the directional gradient response. These four sets of features are used for the authentication of users from two databases using Euclidean Distance, Chi square measure and Support Vector Machines as classifiers.

References
  1. Ying-Han Pang, Andrew TeohBeng Jin, David Ngo Chek Ling, "Palmprint based Cancelable Biometric Authentication System", Int. J. of Signal Processing, Vol.1, No.2, pp.93-99, 2004.
  2. N. Duta, A.K. Jain, and K.V. Mardia, “Matching of Palmprint,” Pattern Recognition Letters, Vol.23, No. 4, pp. 477–485, 2001.
  3. Ying-Han Pang, Andrew Teoh Beng Jin, David Ngo Chek Ling, Fu San Hiew, "Palmprint Verification with Moments", WSCE, 2004.
  4. Lei Zhang, Zhenhua Guo, Zhou Wang, David Zhang, "Palmprint Verification Using Complex Wavelet Transform", IEEE Intl. Conf. on Image Processing, San Antonio, TX, Sept.16-19,2007.
  5. Li Fang, Maylor K.H. Leung , Tejas Shikhare, Victor Chan, Kean FattChoon, “Palmprint Classification”, IEEE Int. Conf. on Systems, Manand Cybernetics, Vol. 4, 8-11 October 2006, pp.2965-2969.
  6. David Zhang, Wai-Kin Kong, Jane You, and Michael Wong, “Online Palmprint Identification”, IEEE Trans. Pattern Analysis &Machine Intelligence, Vol. 25, No. 9, pp. 1041 – 1050,September 2003.
  7. M. Hanmandlu, H.M. Gupta, Neha Mittal, and S. Vasikarla, "An Authentication System Based on Palmprint", in Proc. ITNG, IEEE Computer Society, 2009, pp.399-404.
  8. Madasu Hanmandlu, Ritu Vijay, Neha Mittal, “A Study of Some New Features for Palmprint Authentication”, Proceedings of The World Congress on Engineering, WCE-2011, 6-8 July, 2011, London, U.K., pp 1623-1628.
  9. E. Boonchien, W. Boonchieng, and R. Kanjanavani, “Edge-Detection and Segmentation Methods for Two-Dimensional Echocardiograms”, Proc. Int'l Conf. Computers in Cardiology, pp. 541-544, September 2004.
  10. T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns”, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 24, pp. 469-481, July 2002.
  11. Michael Goh Kah Ong, Connie Tee and Andrew TeohBeng Jin, “Touch-less Palm-print Biometric System”, in Proc.VISIAPP’08, 2008, pp. 423-430.
  12. V.N. Vapnik, Statistical Learning Theory. New York: Wiley-Interscience, 1998.
  13. B. Scholkopf, C.J.C. Burges, and A.J. Smola, Advances in kemel Methods-Support Vector Learning. Cambridge, MA: MIT Press, 1998.
  14. H. Li, Y. Liang, and Q. Xu, “Support Vector Machines and its Applications in Chemistry”, Chemometrics and Intelligent Laboratory Systems, vol. 95, pp. 188-198, Feb. 2009.
  15. Chih-Chung Chang and Chih-Jen Lin, LIBSVM: a library for support vector machines, 2001. http://www.csie.ntu.edu.tw/~cjlin/libsvm
  16. H. K. Polytechnic University, “Palmprint database”, Biometric Research Center Website. 2005. http://www4.comp.polyu.edu.hk/~biometrics/
  17. A. Kong and D. Zhang, “Competitive coding scheme for palmprint verification,” in Proc. Int. Conf. Pattern Recog., 2004, pp. 520–523, Cambride, UK.
  18. Xiang-Qian Wu, Kuan-Quan Wang, David Zhang, ”Wavelet Based Palmprint Recognition”, First international conference on Machine learning and Cybernetics, Beijing, pp-1253-1257, 4-5 Nov 2002.
  19. Xian-Qian Wu, Kuan-Quan Wang, David Zhang, “Wavelet based Palmprint Recognition”, Proceedings of the first international conference on machine learning and cybernetics, Beijing, 4-5 November 2002.
Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy features Wavelet features sigmoid feature Local Binary Pattern Support Vector Machines