Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics
<|reference_start|>Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics: 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.<|reference_end|>
@article{yucer2024disentangling,
    title={Disentangling Racial Phenotypes: Fine-Grained Control of Race-related
  Facial Phenotype Characteristics},
    author={Seyma Yucer, Amir Atapour Abarghouei, Noura Al Moubayed, Toby P.
  Breckon},
    journal={arXiv preprint arXiv:2403.19897},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.19897},
    primaryClass={cs.CV cs.LG}
}