Robust age estimation model using group‐aware contrastive learning

Abstract

Although great efforts have been devoted to developing lightweight models for age estimation in recent works, the robustness is still unsatisfactory in unconstrained environments. This paper proposes a Group-aware Contrastive Network (GACN), a robust lightweight model, which extracts discriminative features by leveraging contrastive learning rather than increasing model parameters. Specifically, with a carefully designed contrastive loss function, GACN minimizes intra-class distances and maximizes inter-class distances between different age groups in feature space. Thus, faces belonging to the same age group are pulled together, while clusters of faces from different age groups are pushed apart. Unlike existing contrastive learning methods, which are separated from the downstream tasks, GACN integrates contrastive learning into age regression and jointly optimizes them for age representation learning. This allows to achieve robust age estimation using a lightweight network that is 1/662 of the model size of VGGNet. Extensive experiments on IMDB-WIKI, Morph II, and FG-NET demonstrate that the proposed method has a significant improvement over the baseline model and performs comparably to existing compact and bulky methods.