  Deep learning-based face recognition continues to face challenges due to its
reliance on huge datasets obtained from web crawling, which can be costly to
gather and raise significant real-world privacy concerns. To address this
issue, we propose VIGFace, a novel framework capable of generating synthetic
facial images. Initially, we train the face recognition model using a real face
dataset and create a feature space for both real and virtual IDs where virtual
prototypes are orthogonal to other prototypes. Subsequently, we generate
synthetic images by using the diffusion model based on the feature space. Our
proposed framework provides two significant benefits. Firstly, it allows for
creating virtual facial images without concerns about portrait rights,
guaranteeing that the generated virtual face images are clearly differentiated
from existing individuals. Secondly, it serves as an effective augmentation
method by incorporating real existing images. Further experiments demonstrate
the efficacy of our framework, achieving state-of-the-art results from both
perspectives without any external data.
