  In this work we propose a novel deep-learning approach for age estimation
based on face images. We first introduce a dual image augmentation-aggregation
approach based on attention. This allows the network to jointly utilize
multiple face image augmentations whose embeddings are aggregated by a
Transformer-Encoder. The resulting aggregated embedding is shown to better
encode the face image attributes. We then propose a probabilistic hierarchical
regression framework that combines a discrete probabilistic estimate of age
labels, with a corresponding ensemble of regressors. Each regressor is
particularly adapted and trained to refine the probabilistic estimate over a
range of ages. Our scheme is shown to outperform contemporary schemes and
provide a new state-of-the-art age estimation accuracy, when applied to the
MORPH II dataset for age estimation. Last, we introduce a bias analysis of
state-of-the-art age estimation results.
