Synthetic Identity

Synthetic identity generation focuses on creating artificial datasets of faces, identities, and documents for research and development purposes, primarily to address ethical and privacy concerns surrounding the use of real-world data in applications like face recognition and identity verification. Current research employs generative adversarial networks (GANs), often enhanced with techniques to control specific attributes like age, gender, and pose, and explores methods to improve the realism and diversity of synthetic data while maintaining identity consistency across multiple samples. This work is crucial for advancing AI applications while mitigating privacy risks and biases inherent in using real-world data, enabling the development of more robust and ethical systems.