Gaze Control

Gaze control research focuses on understanding and replicating the mechanisms by which humans direct their visual attention, aiming to translate this into practical applications. Current research employs deep learning models, including convolutional neural networks and recurrent neural networks like LSTMs, often integrated with computer vision techniques for tasks such as eye-gaze tracking and mapping gaze data to control actions in robots or other systems. This field is significant for advancing human-computer interaction, particularly in areas like assistive technologies, autonomous driving, and social robotics, where accurate and efficient gaze control can improve performance and user experience.