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Efficient and Robust Multimodal Biometric System for Feature Level Fusion (Speech and Signature)

by Dapinder Kaur, Gaganpreet Kaur, Dheerendra Singh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 75 - Number 5
Year of Publication: 2013
Authors: Dapinder Kaur, Gaganpreet Kaur, Dheerendra Singh
10.5120/13109-0432

Dapinder Kaur, Gaganpreet Kaur, Dheerendra Singh . Efficient and Robust Multimodal Biometric System for Feature Level Fusion (Speech and Signature). International Journal of Computer Applications. 75, 5 ( August 2013), 33-38. DOI=10.5120/13109-0432

@article{ 10.5120/13109-0432,
author = { Dapinder Kaur, Gaganpreet Kaur, Dheerendra Singh },
title = { Efficient and Robust Multimodal Biometric System for Feature Level Fusion (Speech and Signature) },
journal = { International Journal of Computer Applications },
issue_date = { August 2013 },
volume = { 75 },
number = { 5 },
month = { August },
year = { 2013 },
issn = { 0975-8887 },
pages = { 33-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume75/number5/13109-0432/ },
doi = { 10.5120/13109-0432 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:43:29.465647+05:30
%A Dapinder Kaur
%A Gaganpreet Kaur
%A Dheerendra Singh
%T Efficient and Robust Multimodal Biometric System for Feature Level Fusion (Speech and Signature)
%J International Journal of Computer Applications
%@ 0975-8887
%V 75
%N 5
%P 33-38
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A Pattern can be characterized by more or less rich & varied pieces of information of different features. The fusion of these different sources of information can provide an opportunity to develop more efficient biometric system which is known as Multimodal biometric System. Multimodal biometrics is the combination of two or more modalities such as signature and speech modalities. In this work an offline signature verification system and speech verification system are combined as these modalities are widely accepted and natural to produce. This combination of multimodal enhances security and accuracy. In this work, database can be gathered from 14 users. Each user contributes 4 samples of signature & speech also. Forgeries are also added to test system. 14 forgeries are used for testing purpose. SIFT features are extracted for offline signature which results as a feature vector of 128 numbers & MFCC features are extracted for speech which results as a feature vector of 195 numbers. Fusion at feature extraction level is used in this work by using a new technique named msum which can be proposed by combining sum method & mean method. The experimental results demonstrated that the proposed multimodal biometric system achieves a recognition accuracy of 98. 2% and with false rejection rate (FRR) of = 0. 9% & false acceptance rate (FAR) of = 0. 9%.

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Index Terms

Computer Science
Information Sciences

Keywords

Biometric Multimodal Biometrics Scale invariant features transform (SIFT) Mel Frequency Cepstral Coefficient (MFCC) Feature level Fusion False Accept Rate (FAR) False Reject Rate (FRR).

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