Abstract
Age estimation from facial images is typically cast as
a nonlinear regression problem. The main challenge of
this problem is the facial feature space w.r.t. ages is inhomogeneous, due to the large variation in facial appearance across different persons of the same age and the nonstationary property of aging patterns. In this paper, we propose Deep Regression Forests (DRFs), an end-to-end model, for age estimation. DRFs connect the split nodes to a
fully connected layer of a convolutional neural network (CNN) and deal with inhomogeneous data by jointly learning
input-dependant data partitions at the split nodes and data
abstractions at the leaf nodes. This joint learning follows
an alternating strategy: First, by fixing the leaf nodes, the
split nodes as well as the CNN parameters are optimized
by Back-propagation; Then, by fixing the split nodes, the
leaf nodes are optimized by iterating a step-size free update
rule derived from Variational Bounding. We verify the proposed DRFs on three standard age estimation benchmarks
and achieve state-of-the-art results on all of them