CalibrationFree Gaze Zone Estimation Using Convolutional Neural Network In this paper we propose a gaze zone estimation method using deep learning. Compared with traditional method, our method does not need the procedure of calibration. In the proposed method, a Kinect is used to capture the video of a computer user, which is preprocessed to suppress illumination variations. After that, haar cascade classifier is adopted to detect the face region and eye region. Then, the eye region is used to estimate the gaze zone on the monitor via a trained CNN (Convolution Neural Network). Experimental results show that the proposed method has a high accuracy, which can be applied in humancomputer interaction.

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.