Abstract
When synthesizing identities as face recognition training data, it is generally believed that large inter-class separability and intra-class attribute variation are essential for synthesizing a quality dataset. % This belief is generally correct, and this is what we aim for. However, when increasing intra-class variation, existing methods overlook the necessity of maintaining intra-class identity consistency. % To address this and generate high-quality face training data, we propose Vec2Face+, a generative model that creates images directly from image features and allows for continuous and easy control of face identities and attributes. Using Vec2Face+, we obtain datasets with proper inter-class separability and intra-class variation and identity consistency using three strategies: 1) we sample vectors sufficiently different from others to generate well-separated identities; 2) we propose an AttrOP algorithm for increasing general attribute variations; 3) we propose LoRA-based pose control for generating images with profile head poses, which is more efficient and identity-preserving than AttrOP. % Our system generates VFace10K, a synthetic face dataset with 10K identities, which allows an FR model to achieve state-of-the-art accuracy on seven real-world test sets. Scaling the size to 4M and 12M images, the corresponding VFace100K and VFace300K datasets yield higher accuracy than the real-world training dataset, CASIA-WebFace, on five real-world test sets. This is the first time a synthetic dataset beats the CASIA-WebFace in average accuracy. In addition, we find that only 1 out of 11 synthetic datasets outperforms random guessing (\emph{i.e., 50\%}) in twin verification and that models trained with synthetic identities are more biased than those trained with real identities. Both are important aspects for future investigation.
Abstract (translated)
在将身份合成用作面部识别训练数据时，通常认为大类间分离性和类内属性变化对于生成高质量的数据集至关重要。这种看法通常是正确的，这也是我们的目标所在。然而，在增加类内变化的同时，现有方法往往忽略了保持类内身份一致性的重要性。为了应对这一挑战并生成高质量的面部训练数据，我们提出了一种名为Vec2Face+的生成模型，该模型可以直接从图像特征创建图像，并允许对人脸身份和属性进行连续且易于控制的操作。通过使用Vec2Face+，我们采用三种策略获得了具备适当类间分离性和类内变化及一致性身份的数据集：1）采样足够不同于其他样本的向量以生成具有较好隔离性的身份；2）提出了一种AttrOP算法来增加一般属性的变化；3）提出了基于LoRA的姿态控制方法，用于生成带有侧脸姿态的图像，这种方法比AttrOP更高效且更能保持身份一致性。 我们的系统产生了VFace10K，这是一个拥有10,000个不同身份的人工合成面部数据集，在七个真实世界的测试集中使面部识别模型达到了最先进的精度水平。当我们扩展该数据集规模至4M和12M图像时，对应的VFace100K和VFace300K数据集在五个实际测试集合上比现实世界训练数据集CASIA-WebFace的平均准确度更高。这是首次合成数据集在平均精度上超越了CASIA-WebFace。 此外，我们发现只有11个合成数据集中的一项超过了随机猜测（即50%）的表现，在双胞胎验证任务中。另外，使用合成身份训练出的模型比使用真实身份训练出的模型更加偏颇。这两者都是未来研究的重要方面。
URL
https://arxiv.org/abs/2507.17192
