The availability of large-scale authentic face databases has been crucial to the significant advances made in face recognition research over the past decade. However, legal and ethical concerns led to the recent retraction of many of these databases by their creators, raising questions about the continuity of future face recognition research without one of its key resources. Synthetic datasets have emerged as a promising alternative to privacy-sensitive authentic data for face recognition development. However, recent synthetic datasets that are used to train face recognition models suffer either from limitations in intra-class diversity or cross-class (identity) discrimination, leading to less optimal accuracies, far away from the accuracies achieved by models trained on authentic data. This paper targets this issue by proposing IDiff-Face, a novel approach based on conditional latent diffusion models for synthetic identity generation with realistic identity variations for face recognition training. Through extensive evaluations, our proposed synthetic-based face recognition approach pushed the limits of state-of-the-art performances, achieving, for example, 98.00% accuracy on the Labeled Faces in the Wild (LFW) benchmark, far ahead from the recent synthetic-based face recognition solutions with 95.40% and bridging the gap to authentic-based face recognition with 99.82% accuracy.

翻译：过去十年中，大规模真实人脸数据库的可获取性对推动人脸识别研究的重大进展至关重要。然而，法律与伦理问题导致许多此类数据库的创建者近期撤回了数据，这引发了关于未来人脸识别研究在缺失核心资源时能否持续发展的疑问。合成数据集作为隐私敏感型真实数据的替代方案，在人脸识别开发中展现出前景。然而，近期用于训练人脸识别模型的合成数据存在类内多样性不足或类间（身份）区分性有限的问题，导致其识别精度远低于基于真实数据训练的模型。本文针对该问题提出IDiff-Face——一种基于条件潜扩散模型的新方法，通过生成具备逼真身份变化的合成人脸数据，用于人脸识别训练。通过广泛评估，我们提出的基于合成数据的人脸识别方法将当前最优性能推至新高度，例如在Labeled Faces in the Wild (LFW)基准测试中达到98.00%的准确率，远超近期合成人脸识别方案的95.40%，且将差距缩小至基于真实数据的人脸识别（99.82%准确率）。