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Age Group Estimation by Combining Texture and Fractal Analysis

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

N.K.Bansode and P K Sinha. Article: Age Group Estimation by Combining Texture and Fractal Analysis. International Journal of Computer Applications 139(13):29-33, April 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {N.K.Bansode and P.K. Sinha},
	title = {Article: Age Group Estimation by Combining Texture and Fractal Analysis},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {139},
	number = {13},
	pages = {29-33},
	month = {April},
	note = {Published by 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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Keywords

Texture Features , Fractals, Age Group