  Synthetic data is emerging as a substitute for authentic data to solve
ethical and legal challenges in handling authentic face data. The current
models can create real-looking face images of people who do not exist. However,
it is a known and sensitive problem that face recognition systems are
susceptible to bias, i.e. performance differences between different demographic
and non-demographics attributes, which can lead to unfair decisions. In this
work, we investigate how the diversity of synthetic face recognition datasets
compares to authentic datasets, and how the distribution of the training data
of the generative models affects the distribution of the synthetic data. To do
this, we looked at the distribution of gender, ethnicity, age, and head
position. Furthermore, we investigated the concrete bias of three recent
synthetic-based face recognition models on the studied attributes in comparison
to a baseline model trained on authentic data. Our results show that the
generator generate a similar distribution as the used training data in terms of
the different attributes. With regard to bias, it can be seen that the
synthetic-based models share a similar bias behavior with the authentic-based
models. However, with the uncovered lower intra-identity attribute consistency
seems to be beneficial in reducing bias.
