Abstract
Synthetic data has emerged as a practical alternative to authentic face datasets for training face recognition (FR) systems, especially as privacy and legal concerns increasingly restrict the use of real biometric data. Recent advances in identity-conditional diffusion models have enabled the generation of photorealistic and identity-consistent face images. However, many of these models suffer from limited intra-class variation, an essential property for training robust and generalizable FR models. In this work, we propose IDPERTURB, a simple yet effective geometric-driven sampling strategy to enhance diversity in synthetic face generation. IDPERTURB perturbs identity embeddings within a constrained angular region of the unit hyper-sphere, producing a diverse set of embeddings without modifying the underlying generative model. Each perturbed embedding serves as a conditioning vector for a pre-trained diffusion model, enabling the synthesis of visually varied yet identity-coherent face images suitable for training generalizable FR systems. Empirical results demonstrate that training FR on datasets generated using IDPERTURB yields improved performance across multiple FR benchmarks, compared to existing synthetic data generation approaches.
Abstract (translated)
合成数据已经作为一种实用的替代方案出现，用于训练面部识别（FR）系统，特别是在隐私和法律问题越来越多地限制真实生物特征数据使用的情况下。最近，在基于身份条件的扩散模型方面取得的进步使得生成逼真且一致的身份面部图像成为可能。然而，许多这些模型在类内变化方面存在局限性，这对于训练稳健和泛化的面部识别模型来说是一个关键属性。为此，我们提出了IDPERTURB，这是一种简单而有效的几何驱动采样策略，旨在增强合成面部生成的多样性。通过在单位超球体内的限定角度区域内扰动身份嵌入，IDPERTURB能够在不修改基础生成模型的前提下产生多样化的嵌入集。每个被扰动的嵌入都作为一个预训练扩散模型的条件向量，从而能够合成视觉上多样化但又具有身份一致性的面部图像，适用于训练泛化能力更强的面部识别系统。实证结果显示，在多个面部识别基准测试中，使用IDPERTURB生成的数据集进行训练的FR性能优于现有的合成数据生成方法。
URL
https://arxiv.org/abs/2602.18831
