In the field of deep learning applied to face recognition, securing large-scale, high-quality datasets is vital for attaining precise and reliable results. However, amassing significant volumes of high-quality real data faces hurdles such as time limitations, financial burdens, and privacy issues. Furthermore, prevalent datasets are often impaired by racial biases and annotation inaccuracies. In this paper, we underscore the promising application of synthetic data, generated through rendering digital faces via our computer graphics pipeline, in achieving competitive results with the state-of-the-art on synthetic data across multiple benchmark datasets. By finetuning the model,we obtain results that rival those achieved when training with hundreds of thousands of real images (98.7% on LFW [1]). We further investigate the contribution of adding intra-class variance factors (e.g., makeup, accessories, haircuts) on model performance. Finally, we reveal the sensitivity of pre-trained face recognition models to alternating specific parts of the face by leveraging the granular control capability in our platform.

翻译：在深度学习应用于人脸识别领域，获取大规模高质量数据集对于实现精确可靠的结果至关重要。然而，收集大量高质量的真实数据面临着时间限制、经济负担和隐私问题等障碍。此外，现有数据集常受种族偏见和标注不准确的影响。在本文中，我们强调了通过计算机图形学流程渲染数字面部生成的合成数据在多个基准数据集上取得与当前最先进合成数据方法相竞争结果的巨大潜力。通过微调模型，我们获得了与使用数十万张真实图像训练相当的结果（在LFW[1]上达到98.7%）。我们进一步研究了添加类内方差因素（如化妆、配饰、发型）对模型性能的贡献。最后，我们利用平台的精细控制能力，揭示了预训练人脸识别模型对面部特定区域交替变化的敏感性。