Ordinal Deep Feature Learning for Facial Age Estimation

In this paper, we propose an ordinal deep feature learning (ODFL) approach for facial age estimation. Unlike conventional age estimation methods which utilize hand-crafted features, our ODFL develops deep convolutional neural networks to learn discriminative feature descriptors directly from image pixels for face representation. Motivated by the fact that age labels are chronologically correlated and age estimation is an ordinal learning computer vision problem, we enforce two criterions on the descriptors which are learned at the top of our network: 1) the topology-aware ordinal relation of face samples is preserved in the learned feature space, and 2) the age difference information of the embedded feature representation is exploited in a ranking-preserving manner. Extensive experimental results on four face aging datasets show that our approach achieves promising performance compared with the state-of-the-art methods.