AGE GROUP CLASSIFICATION USING MACHINE LEARNING TECHNIQUES

A human face provides a lot of information that allows another person to identify characteristics such as age, sex, etc. Therefore, the challenge is to develop an age group prediction system using the automatic learning method. The task of estimating the age group of the human from their frontal facial images is very captivating, but also challenging because of the pattern of personalized and non-linear aging that differs from one person to another. This paper examines the problem of predicting the age group of the human being based on presenting a facial image with improved accuracy of the estimate. The objective of this study is to construct a framework and later an algorithm that helps to estimate the age group with the reasonable accuracy of facial images. In this paper, we present a method for prediction by age group in which the age group is predicted by the detection of face or face reference points using the Viola - Jones algorithm. After detecting the face, features that include geometric characteristics, wrinkle characteristics and HOG characteristics are extracted, and then these extracted features are used to train a classifier using the neural network. The system used a self-construction database for the age group classification. Finally, the identification rate achieved by the HOG-Neural Network model produces better results.