MetaAge: Meta-Learning Personalized Age Estimators

TL;DR

MetaAge introduces a meta-learning approach to create personalized age estimators by mapping identity features to age estimator parameters, effectively leveraging large-scale datasets without requiring identity labels or multiple samples per person.

Contribution

The paper proposes MetaAge, a novel meta-learning framework that learns to generate personalized age estimators from identity features, overcoming data limitations of existing methods.

Findings

MetaAge significantly improves age estimation accuracy.

Outperforms state-of-the-art personalized age estimation methods.

Effective on multiple benchmark datasets.

Abstract

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.