Design and Implementation of a StyleGAN3-Based System for High-Resolution Synthetic Face Generation

The rapid adoption of facial recognition and biometric authentication systems has created significant challenges related to data security, privacy protection, and ethical use of personal information. Real-world facial datasets often contain sensitive biometric data, raising concerns regarding misuse, identity exposure, and regulatory compliance. To address these challenges, this work proposes a high-resolution synthetic face generation system using StyleGAN3 for security and privacy-oriented applications. The proposed system generates photorealistic facial images at a resolution of 1024×1024 that do not correspond to real individuals, thereby reducing reliance on sensitive biometric datasets. A custom privacy-preserving dataset is designed through careful curation, alignment, normalization, and anonymization of publicly available facial images. StyleGAN3 is employed due to its alias-free architecture and superior geometric consistency, enabling the generation of realistic and diverse synthetic faces suitable for biometric system evaluation, deepfake detection research, and privacy-aware data augmentation. The system incorporates a robust training strategy using adaptive augmentation and regularization to ensure stability and high visual fidelity. Comprehensive evaluation is conducted using quantitative metrics such as Fréchet Inception Distance, Perceptual Path Length, and diversity analysis, along with qualitative human perception studies. The results demonstrate that the proposed system achieves high realism, strong diversity, and consistent facial geometry while minimizing identity memorization risks. By supporting Innovation in synthetic data generation, enabling safer testing of biometric technologies for Accountable institutions, and reducing risks associated with real facial data exposure to protect Human rights, the proposed system contributes to responsible and secure deployment of artificial intelligence in sensitive identity-related domains.