Face De Identification

Face de-identification aims to remove identifying information from facial images while preserving other useful attributes like expression and pose, addressing growing privacy concerns in the age of ubiquitous cameras and facial recognition technology. Current research focuses on developing robust and high-fidelity methods, often employing generative adversarial networks (GANs) like StyleGAN and incorporating techniques like identity disentanglement and attribute retention to improve realism and maintain image utility. These advancements are crucial for balancing privacy protection with the continued use of facial data in various applications, including medical AI and security systems.