AUTOMATIC AGE ESTIMATION AND GENDER CLASSIFICATION IN THE WILD

Abstract

Automatic age estimation and gender classification through facial images are attractive topics in computer vision. They can be used in many real-life applications such as face recognition and internet safety for minors. In this paper, we present a novel approach for age estimation and gender classification under uncontrolled conditions following the standard protocols for fair comparaison. Our proposed approach is based on Multi Level Local Binary Pattern (ML-LBP) features which are extracted from normalized face images. Two different Support Vector Machines (SVM) models are used to predict the age group and the gender of a person. The experimental results on benchmark Image of Groups dataset showed the superiority of our approach compared to that of the state-ofthe- art methods.