Deep Cost-Sensitive and Order-Preserving Feature Learning for
Cross-Population Age Estimation
Abstract
Facial age estimation from a face image is an important
yet very challenging task in computer vision, since humans
with different races and/or genders, exhibit quite different
patterns in their facial aging processes. To deal with the
influence of race and gender, previous methods perform age
estimation within each population separately. In practice,
however, it is often very difficult to collect and label suf-
ficient data for each population. Therefore, it would be
helpful to exploit an existing large labeled dataset of one
(source) population to improve the age estimation performance on another (target) population with only a small labeled dataset available. In this work, we propose a Deep
Cross-Population (DCP) age estimation model to achieve
this goal. In particular, our DCP model develops a twostage training strategy. First, a novel cost-sensitive multitask loss function is designed to learn transferable aging
features by training on the source population. Second, a
novel order-preserving pair-wise loss function is designed
to align the aging features of the two populations. By doing
so, our DCP model can transfer the knowledge encoded in
the source population to the target population. Extensive
experiments on the two of the largest benchmark datasets
show that our DCP model outperforms several strong baseline methods and many state-of-the-art methods.