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Reseach Article

Feature Level Fusion for Fingerprint using Neural Network for Person Identification

Published on January 2018 by Siddiqui Almas, Lothe Savita A., Telgadrupali L., Deshmukh P. D.
International Conference on Cognitive Knowledge Engineering
Foundation of Computer Science USA
ICKE2016 - Number 1
January 2018
Authors: Siddiqui Almas, Lothe Savita A., Telgadrupali L., Deshmukh P. D.
addfb18d-d1b5-483f-8301-437913f738e6

Siddiqui Almas, Lothe Savita A., Telgadrupali L., Deshmukh P. D. . Feature Level Fusion for Fingerprint using Neural Network for Person Identification. International Conference on Cognitive Knowledge Engineering. ICKE2016, 1 (January 2018), 41-45.

@article{
author = { Siddiqui Almas, Lothe Savita A., Telgadrupali L., Deshmukh P. D. },
title = { Feature Level Fusion for Fingerprint using Neural Network for Person Identification },
journal = { International Conference on Cognitive Knowledge Engineering },
issue_date = { January 2018 },
volume = { ICKE2016 },
number = { 1 },
month = { January },
year = { 2018 },
issn = 0975-8887,
pages = { 41-45 },
numpages = 5,
url = { /proceedings/icke2016/number1/28948-6049/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Cognitive Knowledge Engineering
%A Siddiqui Almas
%A Lothe Savita A.
%A Telgadrupali L.
%A Deshmukh P. D.
%T Feature Level Fusion for Fingerprint using Neural Network for Person Identification
%J International Conference on Cognitive Knowledge Engineering
%@ 0975-8887
%V ICKE2016
%N 1
%P 41-45
%D 2018
%I International Journal of Computer Applications
Abstract

Security plays a very important role in one's life. Biometrics is an effective technology for personnel identity authentication. It has the capability to reliably distinguish between an authorized people. This paper presents the fusion of fingerprint modalities at Rank level fusion as well as feature level fusion. This paper includes well-known feature extraction method of Gabor Filter in rank level fusion and minutiae feature extraction method for feature level fusion. Decision making approach is used at rank level and Neural Network approach is used for matching at feature level fusion. Multiple instances for one biometric traits are used. The system activate through artificial neural network. The proposed approach for feature level fusion provides the better result. The recognition rate is increased & the error rate is decreased by with the help of this system.

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Index Terms

Computer Science
Information Sciences

Keywords

Fingerprint Rank-level Fusion Features Level Fusion Neural Network.