Multiple hierarchical decision on neural network to predict human age and gender

Abstract

A person's face provides a lot of information such as age, gender and identity. Faces plays an important role in the estimation/prediction of the age and gender of persons, just by looking at their face. Perceiving human faces and modeling the distinctive features of human faces that contribute most towards face recognition are some of the challenges faced by computer vision and psychophysics researchers. In this research, an attempt is made to classify human age and gender using Multiple Hierarchical decision based on Neural Networks. Now a days, Artificial Neural Network (ANN) has been widely used as a tool for solving many decision modeling problems. In this paper, a feed forward propagation Neural Networks are constructed for human age and gender classification system for gray-scale facial images. Three age groups including Children, Middle-aged adults and Old aged adults and Two gender groups including Male and Female are used in the classification system. The performance of the system is further improved by employing Multiple Hierarchical decision using 3 Sigma Control Limits applied on the output of the Neural Network classifier. The efficiency of the system is demonstrated through the experimental results using benchmark database images.