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.

[1]  Rainer Stiefelhagen,et al.  Neural Network-Based Head Pose Estimation and Multi-view Fusion , 2006, CLEAR.

[2]  Mohan M. Trivedi,et al.  Head Pose Estimation in Computer Vision: A Survey , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Harry Wechsler,et al.  Face pose discrimination using support vector machines (SVM) , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[4]  Paul A. Viola,et al.  Fast Multi-view Face Detection , 2003 .

[5]  Takeo Kanade,et al.  Real-time combined 2D+3D active appearance models , 2004, CVPR 2004.

[6]  Cordelia Schmid,et al.  Human Detection Using Oriented Histograms of Flow and Appearance , 2006, ECCV.

[7]  Javier R. Movellan,et al.  Monocular head pose estimation using generalized adaptive view-based appearance model , 2010, Image Vis. Comput..

[8]  Rainer Stiefelhagen,et al.  Head pose estimation using stereo vision for human-robot interaction , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[9]  Helge J. Ritter,et al.  Recognition of human head orientation based on artificial neural networks , 1998, IEEE Trans. Neural Networks.

[10]  Luc Van Gool,et al.  Real Time Head Pose Estimation from Consumer Depth Cameras , 2011, DAGM-Symposium.

[11]  Ruigang Yang,et al.  Model-based head pose tracking with stereovision , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[12]  Takeo Kanade,et al.  Pose Robust Face Tracking by Combining Active Appearance Models and Cylinder Head Models , 2007, International Journal of Computer Vision.

[13]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[14]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.