Abstract
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.
Abstract (translated)
近年来在深度人脸识别方面的进步引发了对于大量、多样且手动标注的人脸数据的需求。获取真实、高质量的人脸数据对于人脸识别来说证明是一个挑战，主要原因是隐私问题。大型人脸数据集主要来源于网络图片，缺乏明确的用户许可。在本文中，我们研究了合成人脸数据是否可以用于训练效果更好的人脸识别模型，从而减轻数据收集担忧。首先，我们探讨了仅使用合成数据和真实数据训练的最近最先进的人脸识别模型的性能差距。然后，我们通过训练具有各种合成和真实数据组合的最先进骨架，深入探讨了优化后者的验证准确性。最后，我们评估了数据增强方法在合成和真实数据上的效果，目标是相同的。我们的结果表明，在结合数据集训练人脸识别模型时，FR的效果尤为显著，尤其是在与合适的数据增强技术相结合时。
URL
https://arxiv.org/abs/2404.03537
