چکیده (انگلیسی):

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.