Group-aware deep feature learning for facial age estimation

In this paper, we propose a group-aware deep feature learning (GA-DFL) approach for facial age estimation. Unlike most existing methods which utilize hand-crafted descriptors for face representation, our GA-DFL method learns a discriminative feature descriptor per image directly from raw pixels for face representation under the deep convolutional neural networks framework. Motivated by the fact that age labels are chronologically correlated and the facial aging datasets are usually lack of labeled data for each person in a long range of ages, we split ordinal ages into a set of discrete groups and learn deep feature transformations across age groups to project each face pair into the new feature space, where the intra-group variances of positive face pairs from the training set are minimized and the inter-group variances of negative face pairs are maximized, simultaneously. Moreover, we employ an overlapped coupled learning method to exploit the smoothness for adjacent age groups. To further enhance the discriminative capacity of face representation, we design a multi-path CNN approach to integrate the complementary information from multi-scale perspectives. Experimental results show that our approach achieves very competitive performance compared with most stateof-the-arts on three public face aging datasets that were captured under both controlled and uncontrolled environments.