In this work, we develop an appearance-based gaze tracking system allowing user to move their head freely. The main difficulty of the appearance-based gaze tracking method is that the eye appearance is sensitive to head orientation. To overcome the difficulty, we propose a 3-D gaze tracking method combining head pose tracking and appearance-based gaze estimation. We use a random forest approach to model the neighbor structure of the joint head pose and eye appearance space, and efficiently select neighbors from the collected high dimensional data set. Li-optimization is then used to seek for the best solution for regression from the selected neighboring samples. Experiment results shows that it can provide robust binocular gaze tracking results with less constraints but still provides moderate estimation accuracy of gaze estimation.