Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Human age classification using appearance features and artificial neural network

Human age classification using appearance features and artificial neural network This paper presents a novel method for human age classification via face images by a computer. The proposed method classifies the human face images into four age groups: child, young, adult and senior adult by using appearance features as ageing features and artificial neural network (ANN) as age classifier. The appearance features consist of both shape and textural features. Only two geometric ratios in combination with newly introduced rotation, scale and translation invariant efficient feature face angle are used as shape features. Local binary pattern histogram (LBPH) of regions of interest in face images are used as textural features. The ANN is designed by using two layer feedforward backpropagation neural networks. The performance of proposed age classification system is evaluated on face images from FG-NET ageing database and achieved greatly improved accuracy of 91.09% and 88.18% for male and female, respectively. Keywords: age classification; appearance features; artificial neural network; ANN; local binary pattern histogram; LBPH. Reference to this paper should be made as follows: Jagtap, J. and Kokare, M. (2016) `Human age classification using appearance features and artificial neural network', Int. J. Biometrics, Vol. 8, Nos. 3/4, pp.179­201. Biographical notes: Jayant Jagtap received his BE in Electronics and http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Biometrics Inderscience Publishers

Human age classification using appearance features and artificial neural network

Loading next page...
 
/lp/inderscience-publishers/human-age-classification-using-appearance-features-and-artificial-Bu1yKPp42G
Publisher
Inderscience Publishers
Copyright
Copyright © 2016 Inderscience Enterprises Ltd.
ISSN
1755-8301
eISSN
1755-831X
DOI
10.1504/IJBM.2016.082594
Publisher site
See Article on Publisher Site

Abstract

This paper presents a novel method for human age classification via face images by a computer. The proposed method classifies the human face images into four age groups: child, young, adult and senior adult by using appearance features as ageing features and artificial neural network (ANN) as age classifier. The appearance features consist of both shape and textural features. Only two geometric ratios in combination with newly introduced rotation, scale and translation invariant efficient feature face angle are used as shape features. Local binary pattern histogram (LBPH) of regions of interest in face images are used as textural features. The ANN is designed by using two layer feedforward backpropagation neural networks. The performance of proposed age classification system is evaluated on face images from FG-NET ageing database and achieved greatly improved accuracy of 91.09% and 88.18% for male and female, respectively. Keywords: age classification; appearance features; artificial neural network; ANN; local binary pattern histogram; LBPH. Reference to this paper should be made as follows: Jagtap, J. and Kokare, M. (2016) `Human age classification using appearance features and artificial neural network', Int. J. Biometrics, Vol. 8, Nos. 3/4, pp.179­201. Biographical notes: Jayant Jagtap received his BE in Electronics and

Journal

International Journal of BiometricsInderscience Publishers

Published: Jan 1, 2016

There are no references for this article.