Gaze Direction

Gaze direction research aims to accurately determine where a person is looking, a crucial aspect of understanding human behavior and facilitating human-computer interaction. Current research focuses on developing robust and efficient gaze estimation methods using various approaches, including deep learning models (e.g., transformers, CNNs) that leverage facial landmarks, head pose, and even EEG data, often incorporating attention mechanisms and contrastive learning techniques for improved accuracy and generalization. These advancements have significant implications for diverse fields, ranging from assistive robotics and virtual reality to marketing research and the development of more intuitive human-machine interfaces.