SDFD: Building a Versatile Synthetic Face Image Dataset with Diverse Attributes
This addresses the problem of insufficient diversity in training data for face analysis systems, particularly for demographic attribute prediction, though it is incremental as it builds on existing text-to-image models.
The paper tackled the limited diversity in face image datasets for AI systems by proposing a methodology to generate synthetic datasets with broader facial attributes, resulting in a dataset that is equally or more challenging for classification tasks while being much smaller in size.
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.