• Miss. Jagzap Shamli
  • Prof. J.A. Kendule
Keywords: Facial Recognition, Positional Ternary Pattern, GLCM Feature extraction, Principal Component Analysis, Artificial Neural Network, MATLAB 2014.


In this paper, it is proposed to have a method for classification of human age using Artificial Neural Network(ANN) classifier. The classification of human age from facial pictures plays an important role in pc vision, scientific discipline and forensic Science. the various machine and mathematical models, for classifying facial age together with  Principal component Analysis ( PCA), positional Ternary Pattern (PTP) are planned yields higher performance. This paper proposes a completely unique technique of classifying the human age group exploitation Artificial Neural Network. This is often done by pre-processing the face image initially and so extracting the face options exploitation PCA. Then the classification of human age is finished exploitation Artificial Neural Network (ANN). The age is classed into four classes: kid, Young, Middle- aged, and Old. The method of combining PCA and ANN perform higher rather victimization separately.


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How to Cite
Shamli, M. J., & Kendule, P. J. (2018). POSITIONAL TERNARY PATTERN FEATURES BASED HUMAN AGE CLASSIFICATION AND ESTIMATION USING ARTIFICIAL NEURAL NETWORK. Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146, 4(3). Retrieved from https://asianssr.org/index.php/ajct/article/view/692

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