Neural Network based Age and Gender Classification for Facial Images

Abstract

Automatic face identification and verification from facial images attain good accuracy with large sets of training data while face attribute recognition from facial images still remain challengeable. Hence introducing an efficient and accurate facial image classification based on facial attributes is an important task. Therefore paper proposed a methodology for automatic age and gender classification based on feature extraction from facial images. In contrast to the other mechanisms proposed in the literature, this methodology main concern on the biometric feature variation of male and females for the classification. Methodology using two types of features namely, primary and secondary features and it includes three main iterations: Preprocessing, Feature extraction and Classification. This study has been carried out using facial images of age range 8-60 consisting of both gender types. Age classification has done according predefined age ranges including 8-13, 14-25, 26-45 and 46-60. Proposed solution is able to classify images in different lighting conditions and different illumination conditions. Classification is done using Artificial Neural Networks according to the different shape and texture variations of wrinkles on face images. This study has been evaluated and tested on both foreign and Asian face images including both gender types and the four age categories used.