If It's Not Enough, Make It So: Reducing Authentic Data Demand in Face
  Recognition through Synthetic Faces
Andrea Atzori Fadi Boutros Naser Damer Gianni Fenu Mirko Marras
Recent advances in deep face recognition have spurred a growing demand for
large, diverse, and manually annotated face datasets. Acquiring authentic,
high-quality data for face recognition has proven to be a challenge, primarily
due to privacy concerns. Large face datasets are primarily sourced from
web-based images, lacking explicit user consent. In this paper, we examine
whether and how synthetic face data can be used to train effective face
recognition models with reduced reliance on authentic images, thereby
mitigating data collection concerns. First, we explored the performance gap
among recent state-of-the-art face recognition models, trained with synthetic
data only and authentic (scarce) data only. Then, we deepened our analysis by
training a state-of-the-art backbone with various combinations of synthetic and
authentic data, gaining insights into optimizing the limited use of the latter
for verification accuracy. Finally, we assessed the effectiveness of data
augmentation approaches on synthetic and authentic data, with the same goal in
mind. Our results highlighted the effectiveness of FR trained on combined
datasets, particularly when combined with appropriate augmentation techniques.