Face Normalization

Face normalization aims to standardize facial images by removing variations like pose, lighting, and expression, thereby improving the performance of downstream tasks such as facial recognition and landmark detection. Current research focuses on developing novel normalization techniques using various approaches, including neural network-based methods (e.g., employing StyleGANs, spatial transformer networks, and adaptive normalization layers) and algorithmic approaches (e.g., Lennard-Jones layers for point cloud normalization and frequency-domain manipulations). These advancements are crucial for enhancing the robustness, accuracy, and fairness of facial analysis systems across diverse datasets and real-world conditions.