  The performance of automated face recognition systems is inevitably impacted
by the facial aging process. However, high quality datasets of individuals
collected over several years are typically small in scale. In this work, we
propose, train, and validate the use of latent text-to-image diffusion models
for synthetically aging and de-aging face images. Our models succeed with
few-shot training, and have the added benefit of being controllable via
intuitive textual prompting. We observe high degrees of visual realism in the
generated images while maintaining biometric fidelity measured by commonly used
metrics. We evaluate our method on two benchmark datasets (CelebA and AgeDB)
and observe significant reduction (~44%) in the False Non-Match Rate compared
to existing state-of the-art baselines.
