Head Orientation

Head orientation estimation, crucial for various applications from human-computer interaction to medical imaging, focuses on accurately determining the 3D position and rotation of a head. Current research employs deep learning models, including convolutional neural networks (CNNs), graph convolutional networks (GCNs), and recurrent neural networks (RNNs), often incorporating geometric data augmentations and novel loss functions to improve robustness, particularly in challenging scenarios like occlusions and low-resolution data. These advancements are improving accuracy and enabling applications in fields such as robotics, e-learning behavioral analysis, and medical diagnosis, where precise head pose information is critical for effective system design and interpretation.