Face recognition systems have significantly advanced in recent years, driven by the availability of large-scale datasets. However, several issues have recently came up, including privacy concerns that have led to the discontinuation of well-established public datasets. Synthetic datasets have emerged as a solution, even though current synthesis methods present other drawbacks such as limited intra-class variations, lack of realism, and unfair representation of demographic groups. This study introduces GANDiffFace, a novel framework for the generation of synthetic datasets for face recognition that combines the power of Generative Adversarial Networks (GANs) and Diffusion models to overcome the limitations of existing synthetic datasets. In GANDiffFace, we first propose the use of GANs to synthesize highly realistic identities and meet target demographic distributions. Subsequently, we fine-tune Diffusion models with the images generated with GANs, synthesizing multiple images of the same identity with a variety of accessories, poses, expressions, and contexts. We generate multiple synthetic datasets by changing GANDiffFace settings, and compare their mated and non-mated score distributions with the distributions provided by popular real-world datasets for face recognition, i.e. VGG2 and IJB-C. Our results show the feasibility of the proposed GANDiffFace, in particular the use of Diffusion models to enhance the (limited) intra-class variations provided by GANs towards the level of real-world datasets.

翻译：近年来，人脸识别系统因大规模数据集的可用性而取得显著进展。然而，近期浮现出若干问题，包括隐私担忧导致成熟公共数据集的停用。尽管当前合成方法存在类内变体有限、缺乏真实性及人口统计群体代表性不公平等缺陷，合成数据集仍成为解决方案。本研究提出GANDiffFace——一种结合生成对抗网络（GAN）与扩散模型优势的新型人脸识别合成数据集生成框架，旨在突破现有合成数据集的局限。在GANDiffFace中，我们首先利用GAN合成高度逼真的身份特征并满足目标人口分布要求，随后对扩散模型进行微调，使用GAN生成的图像合成同一身份的多幅图像，涵盖多样化的配饰、姿态、表情及场景。通过改变GANDiffFace设置，我们生成了多个合成数据集，并将其匹配与非匹配分数分布与主流真实世界人脸识别数据集（VGG2和IJB-C）的分布进行对比。结果表明，所提出的GANDiffFace具备可行性，尤其通过扩散模型将GAN提供的（有限）类内变体增强至真实世界数据集水平。