SASMU: boost the performance of generalized recognition model using
  synthetic face dataset
Chia-Chun Chung Pei-Chun Chang Yong-Sheng Chen HaoYuan He Chinson Yeh
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.