In this paper, we propose an approach for 3D gaze estimation under head pose variation using RGB-D camera. Our method uses a 3D eye model to determine the 3D optical axis and infer the 3D visual axis. For this, we estimate robustly user head pose parameters and eye pupil locations with an ensembles of randomized trees trained with an important annotated training sets. After projecting eye pupil locations in the sensor coordinate system using the sensor intrinsic parameters and a one-time simple calibration by gazing a known 3D target under different directions, the 3D eyeball centers are determined for a specific user for both eyes yielding the determination of the visual axis. Experimental results demonstrate that our method shows a good gaze estimation accuracy even if the environment is highly unconstrained namely large user-sensor distances (> 1m50) unlike state-of-the-art methods which deal with relatively small distances (< 1m).