Synthetic Face Ageing: Evaluation, Analysis and Facilitation of Age-Robust Facial Recognition Algorithms
Wang Yao, Muhammad Ali Farooq, Joseph Lemley, Peter Corcoran
TL;DR
A notable improvement in the recognition rate of the model trained on synthetic ageing images is demonstrated, with an increase of 3.33% compared to the baseline model when tested on images with a 40-year age gap, which underscores the potential of synthetic age data to enhance the performance of age-invariant face recognition systems.
Abstract
The ability to accurately recognize an individual's face with respect to human aging factor holds significant importance for various private as well as government sectors such as customs and public security bureaus, passport office, and national database systems. Therefore, developing a robust age-invariant face recognition system is of crucial importance to address the challenges posed by ageing and maintain the reliability and accuracy of facial recognition technology. In this research work, the focus is to explore the feasibility of utilizing synthetic ageing data to improve the robustness of face recognition models that can eventually help in recognizing people at broader age intervals. To achieve this, we first design 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 current deep learning-based face recognition algorithm by using synthetic ageing data as well as real ageing data to perform rigorous training and validation. Moreover, these synthetic age data have been used in facilitating face recognition algorithms. Experimental results show that the recognition rate of the model trained on synthetic ageing images is 3.33% higher than the results of the baseline model when tested on images with an age gap of 40 years, which prove the potential of synthetic age data which has been quantified to enhance the performance of age-invariant face recognition systems.