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.

翻译：合成图像合成工作的巨大进展使得能够生成高分辨率和光真化的面部图像。在生物鉴别应用中,使用合成数据的主要动机是解决公开可用的生物鉴别数据的短缺问题,同时减少处理此类敏感信息时的隐私风险。在这项工作中,这些优势被利用,通过模拟人的脸老化和最近面貌变异算法生成配对样本,从而研究老龄化对公开源生物鉴别识别系统工作的影响。此外,还使用一个真实的数据集来评估短期老龄化的影响,将生物鉴别技术的性能与合成领域进行比较。主要调查结果表明,1-5年的短期老龄化对一般识别性能的影响很小。然而,正确核实20年后长期年龄差异的相配面仍是一个重大挑战,需要进一步调查。