SCHIGAND: A Synthetic Facial Generation Mode Pipeline
This work addresses privacy and data scarcity issues in facial biometrics by providing a synthetic dataset generation method, though it appears incremental as it combines existing models.
The paper tackled the problem of generating synthetic facial images for biometric systems by proposing SCHIGAND, a pipeline that integrates multiple models to produce realistic and controllable datasets, achieving a balance between image quality and diversity as evaluated with ArcFace.
The growing demand for diverse and high-quality facial datasets for training and testing biometric systems is challenged by privacy regulations, data scarcity, and ethical concerns. Synthetic facial images offer a potential solution, yet existing generative models often struggle to balance realism, diversity, and identity preservation. This paper presents SCHIGAND, a novel synthetic face generation pipeline integrating StyleCLIP, HyperStyle, InterfaceGAN, and Diffusion models to produce highly realistic and controllable facial datasets. SCHIGAND enhances identity preservation while generating realistic intra-class variations and maintaining inter-class distinctiveness, making it suitable for biometric testing. The generated datasets were evaluated using ArcFace, a leading facial verification model, to assess their effectiveness in comparison to real-world facial datasets. Experimental results demonstrate that SCHIGAND achieves a balance between image quality and diversity, addressing key limitations of prior generative models. This research highlights the potential of SCHIGAND to supplement and, in some cases, replace real data for facial biometric applications, paving the way for privacy-compliant and scalable solutions in synthetic dataset generation.