Facial Identity

Facial identity research focuses on understanding and manipulating representations of faces for various applications, primarily aiming to achieve accurate and robust identity recognition and manipulation while preserving other facial attributes. Current research heavily utilizes generative adversarial networks (GANs) and diffusion models, often incorporating techniques like latent space manipulation, attention mechanisms, and 3D morphable models (3DMMs) to improve identity preservation and control over other facial features like expression and pose. This field is crucial for advancements in biometric security, personalized image generation, deepfake detection, and privacy-preserving technologies, impacting both scientific understanding of face perception and numerous practical applications.