Age Classification in Unconstrained Conditions Using LBP Variants

Automatic age classification from human faces is a challenging task which has recently attained an increasing attention. Most of the proposed approaches have however been mainly concerning controlled settings. In this paper, we propose a novel method for age classification in unconstrained conditions and provide extensive performance evaluation on benchmark datasets with standard protocols, thus allowing a fair comparison and an easy reproduction of the results. Our proposed method is based on a combination of local binary pattern (LBP) variants encoding the structure of elongated facial micro-patterns and their strength. The experimental analysis points out the complexity of the age classification problem under uncontrolled settings. The proposed method provides state-of-the-art performance that can be used as a reference for future investigations.