  AI systems rely on extensive training on large datasets to address various
tasks. However, image-based systems, particularly those used for demographic
attribute prediction, face significant challenges. Many current face image
datasets primarily focus on demographic factors such as age, gender, and skin
tone, overlooking other crucial facial attributes like hairstyle and
accessories. This narrow focus limits the diversity of the data and
consequently the robustness of AI systems trained on them. This work aims to
address this limitation by proposing a methodology for generating synthetic
face image datasets that capture a broader spectrum of facial diversity.
Specifically, our approach integrates a systematic prompt formulation strategy,
encompassing not only demographics and biometrics but also non-permanent traits
like make-up, hairstyle, and accessories. These prompts guide a
state-of-the-art text-to-image model in generating a comprehensive dataset of
high-quality realistic images and can be used as an evaluation set in face
analysis systems. Compared to existing datasets, our proposed dataset proves
equally or more challenging in image classification tasks while being much
smaller in size.
