Eye Gaze Accuracy in the Projectionbased Stereoscopic Display as a Function of Number of Fixation, Eye Movement Time, and Parallax Eye gaze accuracy is one of the most commonly used parameter to evaluate the eye tracker performance. The current study applied stepwise multiple regression to predict the significant predictors for eye gaze accuracy (AC). 7 male and 3 female were recruited to perform multidirectional tapping task in the projectionbased stereoscopic display under 3 different levels of parallax and 6 different levels of index of difficulty (ID). Tobii X2 eye tracker was used to measure the selected four eye movement parameters which consist number of fixation (NF), fixation duration (FD), time to first fixation (TFF), and eye movement time (EMT). The results indicated that NF was found to be the best predictor for AC followed by EMT and parallax. The R2 value of 0.247 indicating that the 24.7% of the variability of the data was explained by the model. Practitioner Summary: The result of multiple regression can be a valuable theoretical foundation for evaluating an eye tracker. The results could be very beneficial for human factors engineers and virtual reality developers especially for predicting eye gaze accuracy.

Regionwise Polynomial Regression for 3D Mobile Gaze Estimation In the context of mobile gaze tracking techniques, a 3D gaze point can be calculated as the middle point between two 3D visual axes. To infer gaze directions and eyeball positions, a nonlinear optimization problem is typically formulated to minimize the angular disparities between the training gaze directions and prediction ones. Nonetheless, the experimental results reported by some previous works show that this kind of approaches are very likely to yield large prediction errors hence considered less useful for humanmachine interactions. In this study, we aim to address this widespread issue in three aspects. At first, instead of using a global regression model, a simple local polynomial model is proposed to backproject a pupil center onto its corresponding visual axis. Based on the LeaveOneOut crossvalidation criterion, the partition structure is automatically learned in the process of resolving a homographylike relationship. Secondly, a good starting point for nonlinearoptimization is obtained by the image eyeball center, which can be estimated by systematic parallax errors. Meanwhile, it is necessary to add the suitable constraints for 3D eye positions. Otherwise, the optimization may end up with trivial solutions, i.e., faraway eye positions. Thirdly, we explore a strategy for designing the spatial distribution of calibration points in a principled manner. The experiment results demonstrate that an encouraging gaze estimation accuracy can be achieved by our proposed framework for both the normal vision and eyewear users.