🤖 AI Summary
This work addresses the challenges in biometric systems arising from stringent privacy regulations and limited access to real facial data, as well as the difficulty of existing generative models in simultaneously achieving high realism, diversity, and identity preservation. To this end, we propose a novel synthetic face generation pipeline that integrates StyleCLIP for semantic control, HyperStyle for high-fidelity reconstruction, InterfaceGAN for attribute editing, and diffusion models for generative capacity into an end-to-end controllable framework. This approach enables high-quality intra-class variation and inter-class distinction while preserving identity consistency. Experimental results demonstrate that the synthesized dataset achieves image quality and diversity comparable to real data under ArcFace verification, effectively supporting biometric system evaluation and showing strong potential to partially replace real-world data.
📝 Abstract
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.