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Palm Print Recognition using Zernike Moments

by Subhajit Karar, Ranjan Parekh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 55 - Number 16
Year of Publication: 2012
Authors: Subhajit Karar, Ranjan Parekh
10.5120/8839-3069

Subhajit Karar, Ranjan Parekh . Palm Print Recognition using Zernike Moments. International Journal of Computer Applications. 55, 16 ( October 2012), 15-19. DOI=10.5120/8839-3069

@article{ 10.5120/8839-3069,
author = { Subhajit Karar, Ranjan Parekh },
title = { Palm Print Recognition using Zernike Moments },
journal = { International Journal of Computer Applications },
issue_date = { October 2012 },
volume = { 55 },
number = { 16 },
month = { October },
year = { 2012 },
issn = { 0975-8887 },
pages = { 15-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume55/number16/8839-3069/ },
doi = { 10.5120/8839-3069 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:57:25.529496+05:30
%A Subhajit Karar
%A Ranjan Parekh
%T Palm Print Recognition using Zernike Moments
%J International Journal of Computer Applications
%@ 0975-8887
%V 55
%N 16
%P 15-19
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper proposes an automated system for recognizing palmprints for biometric identification of individuals. Complex Zernike moments are constructed using a set of complex polynomials which form a complete orthogonal basis set defined on the unit disc. Palmprint images are projected onto the basis set resulting in a set of complex signals. The magnitude of the complex value is computed and a scalar value is derived from it by computing the mean of the vector elements. Classification is done by subtracting the test samples from the mean of the training set. The data set consists of 80 images divided into 4 classes. Accuracy obtained is comparable to the best results reported in literature

References
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Index Terms

Computer Science
Information Sciences

Keywords

Zernike moment Palmprint recognition Texture classification

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