On the Applicability of Synthetic Data for Face Recognition. (arXiv:2104.02815v1 [cs.CV])

Face verification has come into increasing focus in various applications
including the European Entry/Exit System, which integrates face recognition
mechanisms. At the same time, the rapid advancement of biometric authentication
requires extensive performance tests in order to inhibit the discriminatory
treatment of travellers due to their demographic background. However, the use
of face images collected as part of border controls is restricted by the
European General Data Protection Law to be processed for no other reason than
its original purpose. Therefore, this paper investigates the suitability of
synthetic face images generated with StyleGAN and StyleGAN2 to compensate for
the urgent lack of publicly available large-scale test data. Specifically, two
deep learning-based (SER-FIQ, FaceQnet v1) and one standard-based (ISO/IEC TR
29794-5) face image quality assessment algorithm is utilized to compare the
applicability of synthetic face images compared to real face images extracted
from the FRGC dataset. Finally, based on the analysis of impostor score
distributions and utility score distributions, our experiments reveal
negligible differences between StyleGAN vs. StyleGAN2, and further also minor
discrepancies compared to real face images.