Age-group classification using 3DHOG descriptor applied to depth maps

Abstract

Age estimation has lots of real-world applications, such as security control, biometrics, customer relationship management, entertainment and cosmetology. In fact, facial age estimation has gained wide popularity in recent years. Despite numerous research efforts and advances in the last decade, traditional human age-group recognition with the sequence of 2D color images is still a challenging problem. The goal of this work is to recognize human age-group only using depth maps without additional joints information. As a practical solution, we present a novel representation of global appearance of aging-effect such as wrinkles’ depth. The proposed framework relay, first-of-all, on an extended version of Viola-Jones algorithm for face and region of interest (most affected by aging) extraction. Then, the 3D histogram of oriented gradients is used to describe local appearances and shapes of the depth map, for more compact and discriminative aging effect representation. The presented method has been compared with the state-of-the-art 2D-approaches on public datasets. The experimental results demonstrate that our approach achieves a better and more stable performances.