  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.
