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Age Group Estimation by Combining Texture and Fractal Analysis

by N.K.Bansode, P.K. Sinha
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 139 - Number 13
Year of Publication: 2016
Authors: N.K.Bansode, P.K. Sinha
10.5120/ijca2016909524

N.K.Bansode, P.K. Sinha . Age Group Estimation by Combining Texture and Fractal Analysis. International Journal of Computer Applications. 139, 13 ( April 2016), 29-33. DOI=10.5120/ijca2016909524

@article{ 10.5120/ijca2016909524,
author = { N.K.Bansode, P.K. Sinha },
title = { Age Group Estimation by Combining Texture and Fractal Analysis },
journal = { International Journal of Computer Applications },
issue_date = { April 2016 },
volume = { 139 },
number = { 13 },
month = { April },
year = { 2016 },
issn = { 0975-8887 },
pages = { 29-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume139/number13/24553-2016909524/ },
doi = { 10.5120/ijca2016909524 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:40:53.268293+05:30
%A N.K.Bansode
%A P.K. Sinha
%T Age Group Estimation by Combining Texture and Fractal Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 139
%N 13
%P 29-33
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper the age group estimation is presented based on combination of texture and fractal dimension features. The age of the human is used as one of the important key parameter for computer vision applications. The fractal dimension of the face image and the texture analysis is used to classify the age of the person into the three different groups such as child(10-20), young(21-50) and old(51 and above. The proposed approach of combing the fractal and texture features shows an effective estimation of the age group. The facial age groups are estimated with 90% average accuracy.

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Index Terms

Computer Science
Information Sciences

Keywords

Texture Features Fractals Age Group

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