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Personal Identification from Lip-Print Features using a Statistical Model

by Saptarshi Bhattacharjee, S Arunkumar, Samir Kumar Bandyopadhyay
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 55 - Number 13
Year of Publication: 2012
Authors: Saptarshi Bhattacharjee, S Arunkumar, Samir Kumar Bandyopadhyay
10.5120/8817-2801

Saptarshi Bhattacharjee, S Arunkumar, Samir Kumar Bandyopadhyay . Personal Identification from Lip-Print Features using a Statistical Model. International Journal of Computer Applications. 55, 13 ( October 2012), 30-34. DOI=10.5120/8817-2801

@article{ 10.5120/8817-2801,
author = { Saptarshi Bhattacharjee, S Arunkumar, Samir Kumar Bandyopadhyay },
title = { Personal Identification from Lip-Print Features using a Statistical Model },
journal = { International Journal of Computer Applications },
issue_date = { October 2012 },
volume = { 55 },
number = { 13 },
month = { October },
year = { 2012 },
issn = { 0975-8887 },
pages = { 30-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume55/number13/8817-2801/ },
doi = { 10.5120/8817-2801 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:57:09.334628+05:30
%A Saptarshi Bhattacharjee
%A S Arunkumar
%A Samir Kumar Bandyopadhyay
%T Personal Identification from Lip-Print Features using a Statistical Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 55
%N 13
%P 30-34
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a novel approach towards identification of human beings from the statistical analysis of their lip prints. Lip features are extracted by studying the spatial orientations of the grooves present in lip prints of individuals using standard edge detection techniques. Horizontal, vertical and diagonal groove features are analysed using connected-component analysis to generate the region-specific edge datasets. Comparison between test and reference sample datasets against a threshold value to define a match yield satisfactory results. FAR, FRR and ROC metrics have been used to gauge the performance of the algorithm for real-world deployment in unimodal and multimodal biometric verification systems.

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Index Terms

Computer Science
Information Sciences

Keywords

Biometrics Chieloscopy Lip Prints Connected Component Analysis FAR FRR

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