Research Article Open Access

A GAUSSIAN MIXTURE MODEL FOR CLASSIFYING THE HUMAN AGE USING DWT AND SAMMON MAP

J. Nithyashri1 and G. Kulanthaivel2
  • 1 Sathyabama University, India
  • 2 , India

Abstract

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.

Journal of Computer Science
Volume 10 No. 11, 2014, 2292-2298

DOI: https://doi.org/10.3844/jcssp.2014.2292.2298

Submitted On: 8 May 2014 Published On: 20 December 2014

How to Cite: Nithyashri, J. & Kulanthaivel, G. (2014). A GAUSSIAN MIXTURE MODEL FOR CLASSIFYING THE HUMAN AGE USING DWT AND SAMMON MAP. Journal of Computer Science, 10(11), 2292-2298. https://doi.org/10.3844/jcssp.2014.2292.2298

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Keywords

  • Pre-Processing
  • Gamma Correction
  • Contrast Enhancement
  • Facial Aging
  • Feature Extraction