CFCPalsy: Facial Image Synthesis with Cross-Fusion Cycle Diffusion Model for Facial Paralysis Individuals

TL;DR

This paper introduces CFCPalsy, a diffusion-based generative model that synthesizes realistic facial paralysis images to improve dataset availability and aid in diagnosis and treatment.

Contribution

The study presents a novel cross-fusion cycle diffusion model for high-quality facial paralysis image synthesis, addressing dataset scarcity and enhancing diagnostic tools.

Findings

Outperforms existing methods in realism and identity preservation

Generates diverse facial paralysis images with high fidelity

Enhances dataset quality for better machine learning training

Abstract

Currently, the diagnosis of facial paralysis remains a challenging task, often relying heavily on the subjective judgment and experience of clinicians, which can introduce variability and uncertainty in the assessment process. One promising application in real-life situations is the automatic estimation of facial paralysis. However, the scarcity of facial paralysis datasets limits the development of robust machine learning models for automated diagnosis and therapeutic interventions. To this end, this study aims to synthesize a high-quality facial paralysis dataset to address this gap, enabling more accurate and efficient algorithm training. Specifically, a novel Cross-Fusion Cycle Palsy Expression Generative Model (CFCPalsy) based on the diffusion model is proposed to combine different features of facial information and enhance the visual details of facial appearance and texture in facial regions, thus creating synthetic facial images that accurately represent various degrees and types of facial paralysis. We have qualitatively and quantitatively evaluated the proposed method on the commonly used public clinical datasets of facial paralysis to demonstrate its effectiveness. Experimental results indicate that the proposed method surpasses state-of-the-art methods, generating more realistic facial images and maintaining identity consistency.