Publications

This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

Search for Publication


Year(s) from:  to 
Author:
Keywords (separated by spaces):

Real Time Head Pose Estimation with Random Regression Forests

G. Fanelli and J. Gall and L. Van Gool
Computer Vision and Pattern Recognition (CVPR)
Colorad Springs, June 2011

Abstract

Fast and reliable algorithms for estimating the head pose are essential for many applications and higher-level face analysis tasks. We address the problem of head pose estimation from depth data, which can be captured using the ever more affordable 3D sensing technologies available today. To achieve robustness, we formulate pose estimation as a regression problem. While detecting specific face parts like the nose is sensitive to occlusions, learning the regression on rather generic surface patches requires enormous amount of training data in order to achieve accurate estimates. We propose to use random regression forests for the task at hand, given their capability to handle large training datasets. Moreover, we synthesize a great amount of annotated training data using a statistical model of the human face. In our experiments, we show that our approach can handle real data presenting large pose changes, partial occlusions, and facial expressions, even though it is trained only on synthetic neutral face data. We have thoroughly evaluated our system on a publicly available database on which we achieve state-of-the-art performance without having to resort to the graphics card.


Download in pdf format
@InProceedings{eth_biwi_00820,
  author = {G. Fanelli and J. Gall and L. Van Gool},
  title = {Real Time Head Pose Estimation with Random Regression Forests},
  booktitle = {Computer Vision and Pattern Recognition (CVPR)},
  year = {2011},
  month = {June},
  pages = {617-624},
  keywords = {}
}