  The vast progress in synthetic image synthesis enables the generation of
facial images in high resolution and photorealism. In biometric applications,
the main motivation for using synthetic data is to solve the shortage of
publicly-available biometric data while reducing privacy risks when processing
such sensitive information. These advantages are exploited in this work by
simulating human face ageing with recent face age modification algorithms to
generate mated samples, thereby studying the impact of ageing on the
performance of an open-source biometric recognition system. Further, a real
dataset is used to evaluate the effects of short-term ageing, comparing the
biometric performance to the synthetic domain. The main findings indicate that
short-term ageing in the range of 1-5 years has only minor effects on the
general recognition performance. However, the correct verification of mated
faces with long-term age differences beyond 20 years poses still a significant
challenge and requires further investigation.
