  Face recognition applications have grown in parallel with the size of
datasets, complexity of deep learning models and computational power. However,
while deep learning models evolve to become more capable and computational
power keeps increasing, the datasets available are being retracted and removed
from public access. Privacy and ethical concerns are relevant topics within
these domains. Through generative artificial intelligence, researchers have put
efforts into the development of completely synthetic datasets that can be used
to train face recognition systems. Nonetheless, the recent advances have not
been sufficient to achieve performance comparable to the state-of-the-art
models trained on real data. To study the drift between the performance of
models trained on real and synthetic datasets, we leverage a massive attribute
classifier (MAC) to create annotations for four datasets: two real and two
synthetic. From these annotations, we conduct studies on the distribution of
each attribute within all four datasets. Additionally, we further inspect the
differences between real and synthetic datasets on the attribute set. When
comparing through the Kullback-Leibler divergence we have found differences
between real and synthetic samples. Interestingly enough, we have verified that
while real samples suffice to explain the synthetic distribution, the opposite
could not be further from being true.
