SDFD: Building a Versatile Synthetic Face Image Dataset with Diverse Attributes

TL;DR

This paper introduces SDFD, a synthetic face image dataset with diverse attributes created using a prompt-based text-to-image model, enhancing the robustness of face analysis systems by capturing broader facial diversity.

Contribution

The work presents a novel prompt formulation strategy for generating a comprehensive synthetic face dataset with diverse attributes, expanding beyond traditional demographic focus.

Findings

The dataset is more challenging for classification tasks than existing datasets.

Synthetic images are high-quality and realistic.

The dataset is smaller but more diverse.

Abstract

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.