Human Age and Gender Prediction Based on Neural Networks and Three Sigma Control Limits

Abstract

A person’s face provides a lot of information such as age, gender, and identity. Faces play 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 toward face recognition are some of the challenges faced by computer vision and psychophysics researchers. There are many methods have been proposed in the literature for the facial features for age and gender classification. In this research, an attempt is made to classify human age and gender using feed forward propagation neural networks in coarser level. Further final classification is done using 3-sigma control limits in finer level. Proposed approach efficiently classifies three age groups including children, middle-aged adults, and old-aged adults. Similarly two gender groups classified into male and female by the proposed method.The performance of the system is further improved by employing multiple hierarchical decision using three sigma control limits applied on the output of the neural network classifier. The mean and standard deviation has been considered on the output generated from the neural network classifier, and three sigma control limits has been applied to define the range of values for the specific category of age and gender. The efficiency of the system is demonstrated through the experimental results using benchmark database images.