Over the past years, the main research innovations in face recognition
focused on training deep neural networks on large-scale identity-labeled
datasets using variations of multi-class classification losses. However, many
of these datasets are retreated by their creators due to increased privacy and
ethical concerns. Very recently, privacy-friendly synthetic data has been
proposed as an alternative to privacy-sensitive authentic data to comply with
privacy regulations and to ensure the continuity of face recognition research.
In this paper, we propose an unsupervised face recognition model based on
unlabeled synthetic data (USynthFace). Our proposed USynthFace learns to
maximize the similarity between two augmented images of the same synthetic
instance. We enable this by a large set of geometric and color transformations
in addition to GAN-based augmentation that contributes to the USynthFace model
training. We also conduct numerous empirical studies on different components of
our USynthFace. With the proposed set of augmentation operations, we proved the
effectiveness of our USynthFace in achieving relatively high recognition
accuracies using unlabeled synthetic data.

本文提出了一种基于无标注的合成数据的无监督人脸识别模型 (USynthFace)，该模型使用大量的几何和颜色变换以及基于 GAN 的数据增强来训练，实现了相对较高的识别精度。