Face Pose Estimation with Combined 2D and 3D HOG Features
Jiaolong Yang, Wei Ge Liang, Yunde Jia · 2012
This paper describes an approach to location and orientation estimation of a person’s face with color im-age and depth data from a Kinect sensor. The combined 2D and 3D histogram of oriented gradients (HOG) fea-tures, 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 ori-entation estimation over a continuous range. The ex-perimental result demonstrates the effectiveness of the RGBD-HOG feature and our face pose estimation ap-proach. 1.