Human Age Classification and Estimation Based on Positional Ternary Pattern Features Using Ann

Abstract

In this paper, Positional Ternary Pattern features based Human Age classification using Artificial Neural Network for Forensic science application. 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 preprocessing 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 method of combining PCA and ANN performs higher rather than victimization separately.