SCHIGAND生成高保真人脸数据，兼顾真实感、多样性与身份一致性。
SCHIGAND: A Synthetic Facial Generation Mode Pipeline
- 融合多种生成模型，实现可控且逼真的合成人脸生成。
- 在ArcFace测试中表现接近真实数据，平衡了质量与多样性。
- 适合用于隐私合规的生物识别系统训练与测试。
为应对生物识别系统训练与测试中面部数据集面临的隐私法规、数据稀缺和伦理问题，本文提出SCHIGAND合成人脸生成流水线，集成StyleCLIP、HyperStyle、InterfaceGAN与扩散模型，生成高度逼真且可控制的面部数据集。该方法在保持身份一致性的同时，生成类内真实差异和类间明显区分，适用于生物识别测试。通过主流人脸识别模型ArcFace评估生成数据的有效性，实验表明SCHIGAND在图像质量与多样性之间取得良好平衡，克服了以往生成模型的关键局限。本研究展示了其在补充甚至替代真实数据方面的潜力，为合成数据生成提供了符合隐私要求且可扩展的解决方案。
原文摘要 · Abstract (English)
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.
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