AgeDiff: Latent Diffusion-based Face Age Editing with Dual Cross-Attention

The impact of long-term facial ageing on face recognition remains understudied due to a lack of openly available data. The age-related impermanence in facial identity affects many applications, including forensics and border control, limiting the accurate authentication of individuals in large-scale face image datasets. Recent advances in generative models have enabled facial ageing simulation with improved accuracy and identity preservation. In this work, we propose AgeDiff, a latent diffusion-based face age editing model using a Dual Cross-Attention conditioning mechanism to disentangle and control identity and age facial features. We conduct extensive performance evaluations and comparisons to existing methods, exploring how age simulations can assist with face image identification in forensic applications. We will publish our code and pre-trained models upon paper acceptance.