Face pose estimation with combined 2D and 3D HOG features

This paper describes an approach to location and orientation estimation of a person's face with color image and depth data from a Kinect sensor. The combined 2D and 3D histogram of oriented gradients (HOG) features, called RGBD-HOG features, are extracted and used throughout our approach. We present a coarse-to-fine localization paradigm to obtain localization results efficiently using multiple HOG filters trained in support vector machines (SVMs). A feed-forward multi-layer perception (MLP) network is trained for fine face orientation estimation over a continuous range. The experimental result demonstrates the effectiveness of the RGBD-HOG feature and our face pose estimation approach.