A Fingerprint Identification Approach using Neural Networks

A Fingerprint Identification Approach using Neural Networks

P. Sreenivasa Moorthy

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Sreenivasa Moorthy

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α Shri Jagdishprasad Jhabarmal Tibrewala University

A Fingerprint Identification Approach using Neural Networks

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Abstract

Today, because of the vulnerability of standard authentication system, law-breaking has accumulated within the past few years. Identity authentication that relies on biometric feature like face, iris, voice, hand pure mathematics, handwriting, retina, fingerprints will considerably decrease the fraud, so that they square measure being replaced by identity verification mechanisms. Among bioscience, fingerprint systems are one amongst most generally researched and used. it’s fashionable due to their easy accessibility. Moreover in this work the system modified to an adaptive system i.e intelligent by using neural networks.

References

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

P. Sreenivasa Moorthy. 2014. \u201cA Fingerprint Identification Approach using Neural Networks\u201d. Global Journal of Computer Science and Technology - F: Graphics & Vision GJCST-F Volume 14 (GJCST Volume 14 Issue F5): .

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GJCST Volume 14 Issue F5
Pg. 1- 7

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

AFPR biometric security etc gabor filter Neural networks

Submission ReceivedDecember 10, 2013
Peer Review Double Blind
Handling Editor
Accepted January 4, 2014
Published January 15, 2014

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v1.2

Issue date

December 31, 2014

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Article Matrices

Total Score: 102

Country: India

Subject: Global Journal of Computer Science and Technology - F: Graphics & Vision

Authors: P,Sreenivasa Moorthy (PhD/Dr. count: 0)

View Count (all-time): 255

Total Views (Real + Logic): 8537

Total Downloads (simulated): 2300

Publish Date: 2014 12, Wed

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Total Views: 8537

Total Downloads: 2300

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