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Title:Fractal and fingerprint analysis through phase embedded diffraction pattern
Authors:ID Swapna, Mohanachandran Nair Sindhu, UNIVERSITY OF KERALA (Author), et al.
Files: This document has no files that are freely available to the public. This document may have a physical copy in the library of the organization, check the status via COBISS. Link is opened in a new window
Language:English
Work type:Not categorized
Typology:1.01 - Original Scientific Article
Organization:UNG - University of Nova Gorica
Abstract:Fingerprint recognition is the most popular biometric identification method currently employed in security applications. Though there are several optical methods for fingerprint analysis based on intensity recording, phase recording alone can give the precise result. Interference and diffraction phenomenon are capable of recording phase variations. In the present method diffraction patterns corresponding to the fingerprints are generated and the spacing between the ridges is calculated. The spacing between ridges is found to be in agreement with the value measured using the software ImageJ. The fingerprint impressions of more than thirty-five persons are recorded and analyzed using the statistical tools in Matlab.The fingerprints are also subjected to fractal analysis. The program could successfully identify a fingerprint from the database. The present work is the first report of fingerprint analysis from diffraction pattern
Keywords:Diffraction, Skew, Kurtosis, Fractals
Publication version:Version of Record
Year of publishing:2017
Number of pages:5
Numbering:9, 8
PID:20.500.12556/RUNG-7434 New window
COBISS.SI-ID:113397251 New window
NUK URN:URN:SI:UNG:REP:HJVYF8WF
Publication date in RUNG:30.06.2022
Views:1268
Downloads:0
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Record is a part of a journal

Title:International Journal of Engineering Science and Technology
Year of publishing:2017
ISSN:0975-5462

Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:29.06.2022

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