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Feature Selection for Finger Knuckle Print-based Multimodal Biometric System

by Madasu Hanmandlu, Jyotsana Grover
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 38 - Number 10
Year of Publication: 2012
Authors: Madasu Hanmandlu, Jyotsana Grover
10.5120/4725-6905

Madasu Hanmandlu, Jyotsana Grover . Feature Selection for Finger Knuckle Print-based Multimodal Biometric System. International Journal of Computer Applications. 38, 10 ( January 2012), 27-33. DOI=10.5120/4725-6905

@article{ 10.5120/4725-6905,
author = { Madasu Hanmandlu, Jyotsana Grover },
title = { Feature Selection for Finger Knuckle Print-based Multimodal Biometric System },
journal = { International Journal of Computer Applications },
issue_date = { January 2012 },
volume = { 38 },
number = { 10 },
month = { January },
year = { 2012 },
issn = { 0975-8887 },
pages = { 27-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume38/number10/4725-6905/ },
doi = { 10.5120/4725-6905 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:25:02.226538+05:30
%A Madasu Hanmandlu
%A Jyotsana Grover
%T Feature Selection for Finger Knuckle Print-based Multimodal Biometric System
%J International Journal of Computer Applications
%@ 0975-8887
%V 38
%N 10
%P 27-33
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, feature level fusion of finger knuckle prints (FKP’s) is implemented. To overcome the curse of dimensionality, feature selection using the triangular norms is proposed. There has been no effort on feature selection using the t-norms in the literature. In this paper we address the problem of feature selection on the finger knuckle print using the t-norms. An unknown parameter in t-norms is learnt using Reinforced Hybrid evolutionary technique. Feature level fusion is performed by combining the significant features of all FKP’s. Results show an improvement in the accuracy when the features are selected by a divergence function derived from the new entropy function using t-norms on two pairs of training features taken at a time. Results of both identi?cation and veri?cation rates show a signi?cant improvement in the performance with feature level fusion.

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Index Terms

Computer Science
Information Sciences

Keywords

Feature selection feature level fusion triangular norms Finger knuckle print.

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