Facial expression morphing: enhancing visual fidelity and preserving facial details in CycleGAN-based expression synthesis.

ABSTRACT: Recent advancements in facial expression synthesis using deep learning, particularly with Cycle-Consistent Adversarial Networks (CycleGAN), have led to impressive results. However, a critical challenge persists: the generated expressions often lack the sharpness and fine details of the original face, such as freckles, moles, or birthmarks. To address this issue, we introduce the Facial Expression Morphing (FEM) algorithm, a novel post-processing method designed to enhance the visual fidelity of CycleGAN-based outputs. The FEM method blends the input image with the generated expression, prioritizing the preservation of crucial facial details. We experimented with our method on the Radboud Faces Database (RafD) and evaluated employing the Fréchet Inception Distance (FID) standard benchmark f

SUBMITTER: Sub-R-Pa C 

PROVIDER: S-EPMC11623074 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature