Recent advances in deep face recognition have spurred a growing demand for
large, diverse, and manually annotated face datasets. Acquiring authentic,
high-quality data for face recognition has proven to be a challenge, primarily
due to privacy concerns. Large face datasets are primarily sourced from
web-based images, lacking explicit user consent. In this paper, we examine
whether and how synthetic face data can be used to train effective face
recognition models with reduced reliance on authentic images, thereby
mitigating data collection concerns. First, we explored the performance gap
among recent state-of-the-art face recognition models, trained with synthetic
data only and authentic (scarce) data only. Then, we deepened our analysis by
training a state-of-the-art backbone with various combinations of synthetic and
authentic data, gaining insights into optimizing the limited use of the latter
for verification accuracy. Finally, we assessed the effectiveness of data
augmentation approaches on synthetic and authentic data, with the same goal in
mind. Our results highlighted the effectiveness of FR trained on combined
datasets, particularly when combined with appropriate augmentation techniques.

本研究主要探讨利用合成人脸数据训练深度人脸识别模型的有效性，以减少对真实图像的依赖并解决数据收集问题，并且通过数据增强技术的应用进一步优化识别准确率。

通过合成人脸降低人脸识别中真实数据的需求量

If It's Not Enough, Make It So: Reducing Authentic Data Demand in Face  Recognition through Synthetic Faces

While synthetic bilingual corpora have demonstrated their effectiveness in
low-resource neural machine translation (NMT), adding more synthetic data often
deteriorates translation performance. In this work, we propose alternated
training with synthetic and authentic data for NMT. The basic idea is to
alternate synthetic and authentic corpora iteratively during training. Compared
with previous work, we introduce authentic data as guidance to prevent the
training of NMT models from being disturbed by noisy synthetic data.
Experiments on Chinese-English and German-English translation tasks show that
our approach improves the performance over several strong baselines. We
visualize the BLEU landscape to further investigate the role of authentic and
synthetic data during alternated training. From the visualization, we find that
authentic data helps to direct the NMT model parameters towards points with
higher BLEU scores and leads to consistent translation performance improvement.

通过交替使用合成和真实语料库，提出一种神经机器翻译的备选训练方法，实验结果表明，在汉英翻译和德英翻译任务中，该方法在几种强基线模型上都表现出更好的性能，真实数据有助于将 NMT 模型参数导向具有较高 BLEU 分数的点并提高一致性的翻译表现。