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Pamlprint Biometric System for Personal Authentication

Published on November 2014 by S S Tamilchelvi, N B Mahesh Kumar
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International Conference on Innovations in Information, Embedded and Communication Systems
Foundation of Computer Science USA
ICIIECS - Number 1
November 2014
Authors: S S Tamilchelvi, N B Mahesh Kumar
2e0ef175-1dcd-4d15-a589-ae6b7be207d2

S S Tamilchelvi, N B Mahesh Kumar . Pamlprint Biometric System for Personal Authentication. International Conference on Innovations in Information, Embedded and Communication Systems. ICIIECS, 1 (November 2014), 14-17.

@article{
author = { S S Tamilchelvi, N B Mahesh Kumar },
title = { Pamlprint Biometric System for Personal Authentication },
journal = { International Conference on Innovations in Information, Embedded and Communication Systems },
issue_date = { November 2014 },
volume = { ICIIECS },
number = { 1 },
month = { November },
year = { 2014 },
issn = 0975-8887,
pages = { 14-17 },
numpages = 4,
url = { /proceedings/iciiecs/number1/18648-1428/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Innovations in Information, Embedded and Communication Systems
%A S S Tamilchelvi
%A N B Mahesh Kumar
%T Pamlprint Biometric System for Personal Authentication
%J International Conference on Innovations in Information, Embedded and Communication Systems
%@ 0975-8887
%V ICIIECS
%N 1
%P 14-17
%D 2014
%I International Journal of Computer Applications
Abstract

Biometrics refers to technologies that measure and analyzes human body behavioral characteristics such as fingerprints, palmprint, knuckles, hand geometry hand vein, retinas, irises and physiological characteristics such as gait, signature, key stroke etc. for authentication purposes. The palmprint is emerging biometric with the advantages of large Region of Interest (ROI) and chances of damages are very rare. In biometric based authentication decision tree is used for the classification of genuine and imposter classes. If the data is noisy, decision tree leads to false branches called over-fitting of the tree. This requires pruning of tree which will lead to decrease in performance. For that Fuzzy Decision Trees (FDT) is developed for classification of claimed identity into two classes genuine and imposter, hence it termed as Fuzzy Binary Decision Tree (FBDT). Fuzzy gini index and fuzzy entropy is used for selection of tree nodes. Matching training scores are used for automatic generation of fuzzy membership functions.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Decision Tree (dt) Fuzzy Binary Decision Tree (fbdt) Mean-index Splitting Criteria Fuzzy Membership Functions Fuzzy Entropy Fuzzy Gini Index. Keywords Are Your Own Designated Keywords Which Can Be Used For Easy Location Of The Manuscript Using Any Search Engines.