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.

翻译：自动人脸识别系统的性能不可避免地受到面部老化过程的影响。然而，跨多年收集的高质量个人数据集通常规模较小。本文提出、训练并验证了使用潜在文本到图像扩散模型对人脸图像进行合成老化与去老化的方法。我们的模型在少样本训练下即可成功运行，并具有可通过直观文本提示进行控制的额外优势。我们观察到生成图像具有高度视觉真实感，同时在常用指标衡量的生物特征保真度上保持稳定。我们在两个基准数据集（CelebA和AgeDB）上评估了该方法，与现有最先进基线相比，错误非匹配率显著降低约44%。