MetaAge: Meta-Learning Personalized Age Estimators
This addresses the challenge of personalized aging modeling in computer vision, offering a more practical solution by reducing data requirements, though it is incremental as it builds on existing meta-learning and personalized methods.
The paper tackles the problem of personalized age estimation by proposing MetaAge, a meta-learning method that learns to map identity features to age estimator parameters without requiring large-scale datasets with identity labels and multiple samples per person, achieving significant performance improvements over state-of-the-art approaches on benchmark datasets.
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.