INFORMATION CHANGE THE WORLD

International Journal of Modern Education and Computer Science (IJMECS)

ISSN: 2075-0161 (Print), ISSN: 2075-017X (Online)

Published By: MECS Press

IJMECS Vol.5, No.9, Oct. 2013

Age Dependent Face Recognition using Eigenface

Full Text (PDF, 247KB), PP.38-44


Views:93   Downloads:3

Author(s)

Hlaing Htake Khaung Tin

Index Terms

Age prediction, face recognition, gender prediction, eigenface, eigenvectors

Abstract

Face recognition is the most successful form of human surveillance. Face recognition technology, is being used to improve human efficiency when recognition faces, is one of the fastest growing fields in the biometric industry. In the first stage, the age is classified into eleven categories which distinguish the person oldness in terms of age. In the second stage of the process is face recognition based on the predicted age. Age prediction has considerable potential applications in human computer interaction and multimedia communication. In this paper proposes an Eigen based age estimation algorithm for estimate an image from the database. Eigenface has proven to be a useful and robust cue for age prediction, age simulation, face recognition, localization and tracking. The scheme is based on an information theory approach that decomposes face images into a small set of characteristic feature images called eigenfaces, which may be thought of as the principal components of the initial training set of face images. The eigenface approach used in this scheme has advantages over other face recognition methods in its speed, simplicity, learning capability and robustness to small changes in the face image.

Cite This Paper

Hlaing Htake Khaung Tin,"Age Dependent Face Recognition using Eigenface", IJMECS, vol.5, no.9, pp.38-44, 2013.DOI: 10.5815/ijmecs.2013.09.06

Reference

[1]P.Philips, "The FERET database and evaluation procedure for face recognition algorithm, "Image and Vision Computing, vol.16, no.5, pp.295-306, 1998.

[2]Horng, W., Lee, C. and Chen, C. "Classification of Age Groups Based on Facial Features", Journal of Science and Engineering, Vol. 4, No. 3, pp. 183-192, 2001.

[3]Fukai, H., Nishie, Y., Abiko , K., Mitsukura, Y., Fukumi, M. and Tanaka, M. "An Age Estimation System on the AIBO", International Conference On Control, Automation And Systems, pp.2551-2554, 2008.

[4]Geng, X., Zhou, Z. and Smith-Miles, K. "Automatic Age Estimation Based on Facial Aging Patterns", IEEE Transaction On Pattern Analysis And Machine Intelligence, Vol. 29, No. 12, pp.2234-2240, December 2007.

[5]Geng, X., Smith-Miles, K. and Zhou, Z. "Facial Age Estimation by Nonlinear Aging Pattern Subspace", Proceedings Of The 16th ACM International Conference on Multimedia , pp. 721-724, 2008.

[6]Luu, K., Ricanek Jr., K. and Y. Suen, C. "Age Estimation using Active Appearance Models and Support Vector Machine Regression", Proceedings Of The IEEE International Conference On Biometrics :Theory Applications And System , pp.314-318 ,2009.