Nowadays, deploying a robust face recognition product becomes easy with the development of face recognition techniques for decades. Not only profile image verification but also the state-of-the-art method can handle the in-the-wild image almost perfectly. However, the concern of privacy issues raise rapidly since mainstream research results are powered by tons of web-crawled data, which faces the privacy invasion issue. The community tries to escape this predicament completely by training the face recognition model with synthetic data but faces severe domain gap issues, which still need to access real images and identity labels to fine-tune the model. In this paper, we propose SASMU, a simple, novel, and effective method for face recognition using a synthetic dataset. Our proposed method consists of spatial data augmentation (SA) and spectrum mixup (SMU). We first analyze the existing synthetic datasets for developing a face recognition system. Then, we reveal that heavy data augmentation is helpful for boosting performance when using synthetic data. By analyzing the previous frequency mixup studies, we proposed a novel method for domain generalization. Extensive experimental results have demonstrated the effectiveness of SASMU, achieving state-of-the-art performance on several common benchmarks, such as LFW, AgeDB-30, CA-LFW, CFP-FP, and CP-LFW.

翻译：如今，随着人脸识别技术数十年的发展，部署稳健的人脸识别产品变得轻而易举。不仅个人资料图像验证，最先进的方法也能近乎完美地处理野外图像。然而，由于主流研究成果依赖于海量网络爬取数据，隐私泄露问题迅速加剧，这引发了隐私侵犯的担忧。研究界试图完全摆脱这一困境，通过使用合成数据训练人脸识别模型，但面临严重的域差距问题，仍需访问真实图像和身份标签来微调模型。本文提出SASMU，一种利用合成数据集进行人脸识别的简单、新颖且有效的方法。我们的方法包括空间数据增强（SA）和频谱混合（SMU）。我们首先分析现有用于开发人脸识别系统的合成数据集。随后揭示，在使用合成数据时，大量数据增强有助于提升性能。通过分析先前的频率混合研究，我们提出了一种新颖的域泛化方法。大量实验结果表明了SASMU的有效性，在多个常见基准如LFW、AgeDB-30、CA-LFW、CFP-FP和CP-LFW上达到了最先进的性能。