Most gaze tracking systems are based on the pupil center corneal reflection (PCCR) method using near infrared (NIR) illuminators. One advantage of the PCCR method is the high accuracy it achieves in gaze tracking because it compensates for the pupil center position based on the relative position of corneal specular reflection (SR). However, the PCCR method only works for user head movements within a limited range, and its performance is degraded by the natural movement of the user’s head. To overcome this problem, we propose a gaze tracking method using an ultrasonic sensor that is robust to the natural head movement of users. Experimental results demonstrate that with our compensation method the gaze tracking system is more robust to natural head movements compared to other systems without our method and commercial systems.

Gaze tracking is the technology that identifies a region in space that a user is looking at. Most previous non-wearable gaze tracking systems use a near-infrared (NIR) light camera with an NIR illuminator. Based on the kind of camera lens used, the viewing angle and depth-of-field (DOF) of a gaze tracking camera can be different, which affects the performance of the gaze tracking system. Nevertheless, to our best knowledge, most previous researches implemented gaze tracking cameras without ground truth information for determining the optimal viewing angle and DOF of the camera lens. Eye-tracker manufacturers might also use ground truth information, but they do not provide this in public. Therefore, researchers and developers of gaze tracking systems cannot refer to such information for implementing gaze tracking system. We address this problem providing an empirical study in which we design an optimal gaze tracking camera based on experimental measurements of the amount and velocity of user’s head movements. Based on our results and analyses, researchers and developers might be able to more easily implement an optimal gaze tracking system. Experimental results show that our gaze tracking system shows high performance in terms of accuracy, user convenience and interest.

Gaze tracking systems usually utilize near-infrared (NIR) lights and NIR cameras, and the performance of such systems is mainly affected by external light sources that include NIR components. This is ascribed to the production of additional (imposter) corneal specular reflection (SR) caused by the external light, which makes it difficult to discriminate between the correct SR as caused by the NIR illuminator of the gaze tracking system and the imposter SR. To overcome this problem, a new method is proposed for determining the correct SR in the presence of external light based on the relationship between the corneal SR and the pupil movable area with the relative position of the pupil and the corneal SR. The experimental results showed that the proposed method makes the gaze tracking system robust to the existence of external light.

Gaze tracking is a camera-vision based technology for identifying the location where a user is looking. In general, a calibration process is applied at the initial stage of most gaze tracking systems. This process is necessary to calibrate for the differences in the eyeballs and cornea size of the user, as well as the angle kappa, and to find the relationship between the user’s eye and screen coordinates. It is applied on the basis of the information of the user’s pupil and corneal specular reflection obtained while the user is looking at several predetermined positions on a screen. In previous studies, user calibration was performed using various types of markers and marker display methods. However, studies on estimating the accuracy of gaze detection through the results obtained during the calibration process have yet to be carried out. Therefore, we propose the method for estimating the accuracy of a final gaze tracking system with a near-infrared (NIR) camera by using a fuzzy system based on the user calibration information. Here, the accuracy of the final gaze tracking system ensures the gaze detection accuracy during the testing stage of the gaze tracking system. Experiments were performed using a total of four types of markers and three types of marker display methods. From them, it was found that the proposed method correctly estimated the accuracy of the gaze tracking regardless of the various marker and marker display types applied.