Few-Shot Personalized Age Estimation
This work addresses the need for personalized age estimation in applications like security and healthcare, though it is incremental as it builds on existing age estimation methods by adding personalization.
The paper tackles the problem of personalized age estimation from faces by introducing OpenPAE, the first open benchmark for N-shot personalized age estimation, showing that personalization consistently improves performance with nonlinear methods significantly outperforming simpler alternatives.
Existing age estimation methods treat each face as an independent sample, learning a global mapping from appearance to age. This ignores a well-documented phenomenon: individuals age at different rates due to genetics, lifestyle, and health, making the mapping from face to age identity-dependent. When reference images of the same person with known ages are available, we can exploit this context to personalize the estimate. The only existing benchmark for this task (NIST FRVT) is closed-source and limited to a single reference image. In this work, we introduce OpenPAE, the first open benchmark for $N$-shot personalized age estimation with strict evaluation protocols. We establish a hierarchy of increasingly sophisticated baselines: from arithmetic offset, through closed-form Bayesian linear regression, to a conditional attentive neural process. Our experiments show that personalization consistently improves performance, that the gains are not merely domain adaptation, and that nonlinear methods significantly outperform simpler alternatives. We release all models, code, protocols, and evaluation splits.