Head and camera rotation invariant eye tracking algorithm based on segmented group method of data handling

Eye-gaze tracking through camera is commonly used in a number of areas, such as computer user interface systems, sports science, psychology, and biometrics. The robustness of the head and camera rotation tracking algorithm has been a critical problem in recent years. In this paper, Haar-like features and a modified version of the group method of data handling, as well as segmented regression, are used together to find the base points of the eyes in a facial image. Then, a geometric transformation is applied to detect precise eye-gaze direction. The proposed algorithm is tested on GI4E and Columbia Gaze datasets and compared to other algorithms. The results show adequate accuracy, especially when the head/camera is rotated.