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Multimodal Biometric Feature based Person Classification

Published on March 2012 by Smita Thakre, Kalyani Mamulkar, Prachi Motghare, Pooja Godi, Ujwalla Gawande
International Conference and Workshop on Emerging Trends in Technology
Foundation of Computer Science USA
ICWET2012 - Number 4
March 2012
Authors: Smita Thakre, Kalyani Mamulkar, Prachi Motghare, Pooja Godi, Ujwalla Gawande
5068fc99-ba03-4324-bc53-e55565a44de8

Smita Thakre, Kalyani Mamulkar, Prachi Motghare, Pooja Godi, Ujwalla Gawande . Multimodal Biometric Feature based Person Classification. International Conference and Workshop on Emerging Trends in Technology. ICWET2012, 4 (March 2012), 41-45.

@article{
author = { Smita Thakre, Kalyani Mamulkar, Prachi Motghare, Pooja Godi, Ujwalla Gawande },
title = { Multimodal Biometric Feature based Person Classification },
journal = { International Conference and Workshop on Emerging Trends in Technology },
issue_date = { March 2012 },
volume = { ICWET2012 },
number = { 4 },
month = { March },
year = { 2012 },
issn = 0975-8887,
pages = { 41-45 },
numpages = 5,
url = { /proceedings/icwet2012/number4/5341-1032/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference and Workshop on Emerging Trends in Technology
%A Smita Thakre
%A Kalyani Mamulkar
%A Prachi Motghare
%A Pooja Godi
%A Ujwalla Gawande
%T Multimodal Biometric Feature based Person Classification
%J International Conference and Workshop on Emerging Trends in Technology
%@ 0975-8887
%V ICWET2012
%N 4
%P 41-45
%D 2012
%I International Journal of Computer Applications
Abstract

A Monomodal Biometric system encounters a variety of security problems and presents sometimes unacceptable error rates. Conventional biometric system tends to have larger memory footprint, slower processing speed, and higher implementations and operational costs. Multiple biometric consist in combining two or more biometric modalities in a single identification system to improve the recognition accuracy. Whereas a state of art of framework for multimodal biometric identification system which can be adapted for any type of biometrics to provide smaller memory footprints and faster implementations than the conventional multimodal biometrics systems. In these paper we extract the feature of iris and fingerprint and fuse them at feature level and utilize SVM(Support Vector Machine) classifier for matching purpose to provide a higher accuracy than unimodal system.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Multimodal Biometric fingerprint recognition Iris recognition feature level fusion SVM Classifier