  Recent advances in deep learning have significantly increased the performance
of face recognition systems. The performance and reliability of these models
depend heavily on the amount and quality of the training data. However, the
collection of annotated large datasets does not scale well and the control over
the quality of the data decreases with the size of the dataset. In this work,
we explore how synthetically generated data can be used to decrease the number
of real-world images needed for training deep face recognition systems. In
particular, we make use of a 3D morphable face model for the generation of
images with arbitrary amounts of facial identities and with full control over
image variations, such as pose, illumination, and background. In our
experiments with an off-the-shelf face recognition software we observe the
following phenomena: 1) The amount of real training data needed to train
competitive deep face recognition systems can be reduced significantly. 2)
Combining large-scale real-world data with synthetic data leads to an increased
performance. 3) Models trained only on synthetic data with strong variations in
pose, illumination, and background perform very well across different datasets
even without dataset adaptation. 4) The real-to-virtual performance gap can be
closed when using synthetic data for pre-training, followed by fine-tuning with
real-world images. 5) There are no observable negative effects of pre-training
with synthetic data. Thus, any face recognition system in our experiments
benefits from using synthetic face images. The synthetic data generator, as
well as all experiments, are publicly available.
