Relative Age Position Learning for Face-Based Age Estimation

This study introduces the concept of relative age position learning to supplement the learning process for age estimation models. Drawing inspiration from feature recalibration modules that prioritize features based on their importance, a novel age-based reweighting module is developed to enhance feature representation in the proposed age estimation method. The proposed reweighting module obtains the features of the selected references for each age and further exploits them to reweight the features of the input images based on age importance. The resulting recalibrated features are then assessed for relative age position prediction. In addition, to achieve better generalization performance in age estimation, a gender prediction head is added to create a multi-task learning network that simultaneously predicts the ages and genders of the input images. Through extensive experiments, we demonstrate that the proposed approach outperforms other state-of-the-art age estimation methods on three challenging benchmark datasets for facial age estimation: AgeDB, AFAD, and CACD.