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Feature Level Fusion in Multimodal Biometric Authentication System

by M. Fathima Nadheen, S. Poornima
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 69 - Number 18
Year of Publication: 2013
Authors: M. Fathima Nadheen, S. Poornima
10.5120/12074-8264

M. Fathima Nadheen, S. Poornima . Feature Level Fusion in Multimodal Biometric Authentication System. International Journal of Computer Applications. 69, 18 ( May 2013), 36-40. DOI=10.5120/12074-8264

@article{ 10.5120/12074-8264,
author = { M. Fathima Nadheen, S. Poornima },
title = { Feature Level Fusion in Multimodal Biometric Authentication System },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 69 },
number = { 18 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 36-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume69/number18/12074-8264/ },
doi = { 10.5120/12074-8264 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:30:38.616482+05:30
%A M. Fathima Nadheen
%A S. Poornima
%T Feature Level Fusion in Multimodal Biometric Authentication System
%J International Journal of Computer Applications
%@ 0975-8887
%V 69
%N 18
%P 36-40
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Multimodal systems integrate multiple sources of human information to ensure high level security. Multimodal biometric systems increase the recognition rate of the biometric systems either by reducing the false acceptance rate (FAR) or false rejection rate (FRR). Multiple biometric traits can be combined at feature level. Feature level fusion increases the reliability of the system by preventing the biometric template from modification. In the proposed system, feature level fusion is employed to fuse the feature vectors of iris and ear extracted by Principal Component Analysis technique, which also reduces the dimension of the feature vectors. Finally matching is performed by comparing the test fused feature vectors with all training images using distance measure. This system is developed to study and analyze, whether the performance of multimodal biometric system is improved over unimodal biometric system by attaining 93% success rate when fusion is inclined.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Feature level fusion Genuine Acceptance Rate Morphological operation Principal Component Analysis

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