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arXiv · 2403.19897

Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics

Seyma Yucer·Amir Atapour Abarghouei·Noura Al Moubayed·Toby P. Breckon
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Abstract

Achieving an effective fine-grained appearance variation over 2D facial images, whilst preserving facial identity, is a challenging task due to the high complexity and entanglement of common 2D facial feature encoding spaces. Despite these challenges, such fine-grained control, by way of disentanglement is a crucial enabler for data-driven racial bias mitigation strategies across multiple automated facial analysis tasks, as it allows to analyse, characterise and synthesise human facial diversity. In this paper, we propose a novel GAN framework to enable fine-grained control over individual race-related phenotype attributes of the facial images. Our framework factors the latent (feature) space into elements that correspond to race-related facial phenotype representations, thereby separating phenotype aspects (e.g. skin, hair colour, nose, eye, mouth shapes), which are notoriously difficult to annotate robustly in real-world facial data. Concurrently, we also introduce a high quality augmented, diverse 2D face image dataset drawn from CelebA-HQ for GAN training. Unlike prior work, our framework only relies upon 2D imagery and related parameters to achieve state-of-the-art individual control over race-related phenotype attributes with improved photo-realistic output.

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Seyma Yucer, Amir Atapour Abarghouei, Noura Al Moubayed, Toby P. Breckon. 2024-03-29. Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics. https://arxiv.org/abs/2403.19897

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