  Different people age in different ways. Learning a personalized age estimator
for each person is a promising direction for age estimation given that it
better models the personalization of aging processes. However, most existing
personalized methods suffer from the lack of large-scale datasets due to the
high-level requirements: identity labels and enough samples for each person to
form a long-term aging pattern. In this paper, we aim to learn personalized age
estimators without the above requirements and propose a meta-learning method
named MetaAge for age estimation. Unlike most existing personalized methods
that learn the parameters of a personalized estimator for each person in the
training set, our method learns the mapping from identity information to age
estimator parameters. Specifically, we introduce a personalized estimator
meta-learner, which takes identity features as the input and outputs the
parameters of customized estimators. In this way, our method learns the meta
knowledge without the above requirements and seamlessly transfers the learned
meta knowledge to the test set, which enables us to leverage the existing
large-scale age datasets without any additional annotations. Extensive
experimental results on three benchmark datasets including MORPH II, ChaLearn
LAP 2015 and ChaLearn LAP 2016 databases demonstrate that our MetaAge
significantly boosts the performance of existing personalized methods and
outperforms the state-of-the-art approaches.
