Head Pose Estimation with Combined 2D SIFT and 3D HOG Features

In this paper, an approach is presented to estimate the 3D position and orientation of head from RGB and depth images captured by a commercial sensor Kinect. We use 2D Scale-invariant feature transform (SIFT) features together with 3D histogram of oriented gradients (HOG) features which are extracted in a pair of RGB and depth images captured synchronously, named SIFT-HOG features, to improve the robustness and accuracy of head pose estimation. We apply random forests to formulate pose estimation as a regression problem, due to their power for handling large training data and the high mapping speed. And then the mean-shift method is employed to refine the result obtained by the random forests. The experiment results demonstrate that our approach of head pose estimation is efficient.