Abstract
While the accuracy of face recognition systems has improved significantly in recent years, the datasets used to train these models are often collected through web crawling without the explicit consent of users, raising ethical and privacy concerns. To address this, many recent approaches have explored the use of synthetic data for training face recognition models. However, these models typically underperform compared to those trained on real-world data. A common limitation is that a single generator model is often used to create the entire synthetic dataset, leading to model-specific artifacts that may cause overfitting to the generator's inherent biases and artifacts. In this work, we propose a solution by combining two state-of-the-art synthetic face datasets generated using architecturally distinct backbones. This fusion reduces model-specific artifacts, enhances diversity in pose, lighting, and demographics, and implicitly regularizes the face recognition model by emphasizing identity-relevant features. We evaluate the performance of models trained on this combined dataset using standard face recognition benchmarks and demonstrate that our approach achieves superior performance across many of these benchmarks.
Abstract (translated)
虽然近年来面部识别系统的准确性有了显著提高，但用于训练这些模型的数据集通常是通过网络爬虫收集的，并未获得用户的明确同意，这引发了伦理和隐私方面的担忧。为了解决这个问题，许多近期的方法探索了使用合成数据来训练面部识别模型的可能性。然而，与基于真实世界数据训练的模型相比，这类方法通常表现较差。常见的限制是单个生成器模型通常用于创建整个合成数据集，导致特定于该模型的偏差和伪影，可能导致过度拟合到生成器本身的偏见和伪影上。 在本研究中，我们提出了一种解决方案，即结合使用两种最新的合成面部数据集，这些数据集是由架构不同的骨干网络生成的。这种融合减少了特定于模型的伪影，增强了姿态、光照及人口统计学方面的多样性，并通过强调与身份相关的特征隐式地对面部识别模型进行了正则化处理。 我们评估了在标准面部识别基准测试中使用该组合数据集训练的模型性能，并证明我们的方法在此类基准中的表现明显优于其他方法。
URL
https://arxiv.org/abs/2507.16790
