Synthetic Face Ageing: Evaluation, Analysis and Facilitation of Age-Robust Facial Recognition Algorithms

Establishing the identity of an individual from their facial data is widely adopted across the consumer sector, driven by the use of facial authentication on handheld devices. This widespread use of facial authentication technology has raised other issues, in particular those of biases in the underlying algorithms. Initial studies focused on ethnic or gender biases, but another area is that of age-related biases. This research work focuses on the challenge of face recognition over decades-long time intervals and explores the feasibility of utilizing synthetic ageing data to improve the robustness of face recognition models in recognizing people across these longer time intervals. To achieve this, we first design a set of experiments to evaluate state-of-the-art synthetic ageing methods. In the next stage, we explore the effect of age intervals on a reference face recognition algorithm using both synthetic and real ageing data to perform rigorous validation. We then use these synthetic age data as an augmentation method to facilitate the age-invariant face recognition algorithm. Extensive experimental results demonstrate a notable improvement in the recognition rate of the model trained on synthetic ageing images, with an increase of 3.33% compared to the baseline model when tested on images with a 40-year age gap. Additionally, our models exhibit competitive performance when validated on benchmark cross-age datasets and general face recognition datasets. These findings underscore the potential of synthetic age data to enhance the performance of age-invariant face recognition systems.