Abstract
In this paper, we propose a novel unified network
named Deep Hybrid-Aligned Architecture for facial age estimation. It contains global, local and
global-local branches, which are jointly optimized
and thus can capture multiple types of features with
complementary information. In each sub-network
of each branch, we employ a separate loss to extract
the independent region features and use a recurrent
fusion to explore correlations among them. Considering that pose variations may lead to misalignment in different regions, we design an Aligned
Region Pooling operation to generate aligned region features. Moreover, a new large private age
dataset named Web-FaceAge owning more than
120K samples is collected under diverse scenes and
spanning a large age range. Experiments on five
age benchmark datasets, including Web-FaceAge,
Morph, FG-NET, CACD and Chalearn LAP 2015,
show that the proposed method outperforms the
state-of-the-art approaches significantly