  Recent deep face recognition models proposed in the literature utilized
large-scale public datasets such as MS-Celeb-1M and VGGFace2 for training very
deep neural networks, achieving state-of-the-art performance on mainstream
benchmarks. Recently, many of these datasets, e.g., MS-Celeb-1M and VGGFace2,
are retracted due to credible privacy and ethical concerns. This motivates this
work to propose and investigate the feasibility of using a privacy-friendly
synthetically generated face dataset to train face recognition models. Towards
this end, we utilize a class-conditional generative adversarial network to
generate class-labeled synthetic face images, namely SFace. To address the
privacy aspect of using such data to train a face recognition model, we provide
extensive evaluation experiments on the identity relation between the synthetic
dataset and the original authentic dataset used to train the generative model.
Our reported evaluation proved that associating an identity of the authentic
dataset to one with the same class label in the synthetic dataset is hardly
possible. We also propose to train face recognition on our privacy-friendly
dataset, SFace, using three different learning strategies, multi-class
classification, label-free knowledge transfer, and combined learning of
multi-class classification and knowledge transfer. The reported evaluation
results on five authentic face benchmarks demonstrated that the
privacy-friendly synthetic dataset has high potential to be used for training
face recognition models, achieving, for example, a verification accuracy of
91.87\% on LFW using multi-class classification and 99.13\% using the combined
learning strategy.
