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Face verification has come into increasing focus in various applicationsincluding the European Entry/Exit System, which integrates face recognitionmechanisms. At the same time, the rapid advancement of biometric authenticationrequires extensive performance tests in order to inhibit the discriminatorytreatment of travellers due to their demographic background. However, the useof face images collected as part of border controls is restricted by theEuropean General Data Protection Law to be processed for no other reason thanits original purpose. Therefore, this paper investigates the suitability ofsynthetic face images generated with StyleGAN and StyleGAN2 to compensate forthe urgent lack of publicly available large-scale test data. Specifically, twodeep learning-based (SER-FIQ, FaceQnet v1) and one standard-based (ISO/IEC TR29794-5) face image quality assessment algorithm is utilized to compare theapplicability of synthetic face images compared to real face images extractedfrom the FRGC dataset. Finally, based on the analysis of impostor scoredistributions and utility score distributions, our experiments revealnegligible differences between StyleGAN vs. StyleGAN2, and further also minordiscrepancies compared to real face images.

On the Applicability of Synthetic Data for Face Recognition