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Fingerprint Classification using KMCG Algorithm under Varying Window and Codebook Sizes

by Winnie Gift Odongo, Waweru Mwangi, Richard Rimiru
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 51
Year of Publication: 2018
Authors: Winnie Gift Odongo, Waweru Mwangi, Richard Rimiru
10.5120/ijca2018917285

Winnie Gift Odongo, Waweru Mwangi, Richard Rimiru . Fingerprint Classification using KMCG Algorithm under Varying Window and Codebook Sizes. International Journal of Computer Applications. 179, 51 ( Jun 2018), 15-22. DOI=10.5120/ijca2018917285

@article{ 10.5120/ijca2018917285,
author = { Winnie Gift Odongo, Waweru Mwangi, Richard Rimiru },
title = { Fingerprint Classification using KMCG Algorithm under Varying Window and Codebook Sizes },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2018 },
volume = { 179 },
number = { 51 },
month = { Jun },
year = { 2018 },
issn = { 0975-8887 },
pages = { 15-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number51/29523-2018917285/ },
doi = { 10.5120/ijca2018917285 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:58:56.224506+05:30
%A Winnie Gift Odongo
%A Waweru Mwangi
%A Richard Rimiru
%T Fingerprint Classification using KMCG Algorithm under Varying Window and Codebook Sizes
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 51
%P 15-22
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Fingerprints are the most widely used form of biometric identification. Fingerprint identification has become time-consuming because of growing size of fingerprint databases. Fingerprint classification can be one of the significant preprocessing steps to improve the accuracy of fingerprint identification systems and is done to put a given fingerprint to one of the existing classes. Classifying fingerprint images is a very difficult pattern recognition problem, due to the possible problem with accuracy which is a measure of how well the system is able to correctly match the biometric information from the same person and avoid falsely matching biometric information from different people. In this research an experiment was conducted and a comparative analysis based on vector quantization for fingerprint classification using Kekre’s Median Codebook Generation (KMCG) was done using codebook sizes 2, 4, 8 and window sizes 2*2, 4*4, 8*8, 16*16, 32*32, 64*64. KMCG is one of the better and faster vector quantization codebook generation methods. Fingerprint images were obtained from the National Institute of Standards and Technology (NIST) special database 4 for this study. It was observed that the method effectively improves the computation speed and provides accuracy of A (Arch) by 99%, TA (Tented Arch) by 98%, LL (Left Loop) by100%, RL (Right Loop) by 100% for codebook size 4 and LL (Left Loop) by 99% accuracy for codebook size 8 and window size 8*8. Codebook size 2, 4 exhibited overall better percentage accuracy of classification than codebook size 8.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Vector Quantization KMCG NIST Fingerprint Classification.

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