A GAUSSIAN MIXTURE MODEL FOR CLASSIFYING THE HUMAN AGE USING DWT AND SAMMON MAP
J. Nithyashri and G. Kulanthaivel
DOI : 10.3844/jcssp.2014.2292.2298
Journal of Computer Science
Volume 10, Issue 11
The appearance of a human face rigorously changes with respect to age that makes Age Classification as a more challenging task. The algorithms such as, K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), Radial Basis Function (RBF), motivated many Face Researchers to focus their attention in classifying the human age into various age groups. The Classification rate produced by these existing algorithms is not significant indeed. In this study, Gaussian Mixture Models (GMM) is used for classifying the facial images into different age groups. A combination of Discrete Wavelet Transformation (DWT) and Sammon Map are used to extract the facial features. The performance of this approach is tested using Album-2 of MORPH database. A maximum classification rate of 99.52% is achieved in stage-1, whereas 99.46% is achieved in stage-2 using GMM. Also the accuracy achieved using Gaussian Mixture Model, is comparatively greater than K-NN.
© 2014 J. Nithyashri and G. Kulanthaivel. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.