CFCPalsy: Facial Image Synthesis with Cross-Fusion Cycle Diffusion Model for Facial Paralysis Individuals
This addresses the scarcity of data for training machine learning models in facial paralysis diagnosis, which is an incremental improvement for medical imaging applications.
This study tackled the problem of limited facial paralysis datasets for automated diagnosis by synthesizing high-quality facial images using a novel diffusion model, achieving more realistic results and identity consistency compared to state-of-the-art methods.
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.