Joint gender, ethnicity and age estimation from 3D faces: An experimental illustration of their correlations.

Humans present clear demographic traits which allow their peers to recognize their gender and ethnic groups as well as estimate their age. Abundant literature has investigated the problem of automated gender, ethnicity and age recognition from facial images. However, despite the co-existence of these traits, most of the studies have addressed them separately, very little attention has been given to their correlations. In this work, we address the problem of joint demographic estimation and investigate the correlation through the morphological differences in 3D facial shapes. To this end, a set of facial features are extracted to capture the 3D shape differences among the demographic groups. Then, a correlation-based feature selection is applied to highlight salient features and remove redundancy. These features are later fed to Random Forest for gender and ethnicity classification, and age estimation. Extensive experiments conducted on FRGCv2 dataset, under Expression-Dependent and Expression-Independent settings, demonstrate the effectiveness of the proposed approaches for the three traits, and also show the accuracy improvement when considering their correlations. To the best of our knowledge, this is the first study exploring the correlations of these facial soft-biometric traits using 3D faces. This is also the first work which studies the problem of age estimation from 3D Faces. 1 1 Part of this work has been published in the International Conference on Computer Vision Theory and Applications 2014 [61] and won the Best Paper Award in the area of Image and Video Understanding . [ABSTRACT FROM AUTHOR]