Dirichlet-tree Distribution Enhanced Random Forests for Head Pose Estimation

Yuanyuan Liu, Jingying Chen, Leyuan Liu, Yujiao Gong, Nan Luo

Abstract

Head pose estimation is important in human-machine interfaces. However, illumination variation, occlusion and low image resolution make the estimation task difficult. Hence, a Dirichlet-tree distribution enhanced Random Forests approach (D-RF) is proposed in this paper to estimate head pose efficiently and robustly under various conditions. First, Gabor features of the facial positive patches are extracted to eliminate the influence of occlusion and noise. Then, the D-RF is proposed to estimate the head pose in a coarse-to-fine way. In order to improve the discrimination capability of the approach, an adaptive Gaussian mixture model is introduced in the tree distribution. The proposed method has been evaluated with different data sets spanning from -90º to 90º in vertical and horizontal directions under various conditions.The experimental results demonstrate the approach’s robustness and efficiency.

References

  1. Breiman, L. (2001). Random forests. In Machine Learning.
  2. Chen, J. and Chen, D. (2011). A feature-based detection and tracking system for gaze and smiling behaviours. In International Journal of Computer Systems Science Engineering. 3: 207214.
  3. Dantone, M. and Gall, J. (2012). Real time facial feature detection using conditional regression forests. In CVPR.
  4. Fanelli, G. and Gall, J. (2011). Real time head pose estimation with random regression forests. In CVPR.
  5. Fanelli, G. and Weise, T. (2011). Real time head pose estimation from consumer depth cameras. In DAGM.
  6. Figueiredo, M. and Jain, A. (2002). Unsupervised learning of finite mixture models. In IEEE Transaction on Pattern Analysis and Machine Intelligence.
  7. Gall, J. and Lempitsky, V. (2009). Class-specic hough forests for object detection. In CVPR.
  8. Gourier, N. and Hall, D. (2004). Estimating face orientation from robust detection of salient facial features in pointing 2004. In ICPR international Workshop on Visual Observation of Deictic Gestures.
  9. Huang, C. and Ding, X. (2010). Head pose estimation based on random forests for multiclass classification. In ICPR.
  10. Huang, G. and Ramesh, T. (2007). Learned-miller. labeled faces in the wild:a database for studying face recognition in unconstrained environments. In Technical report, University of Massachusetts.
  11. Li, Y. and Wang, S. (2010). Person-independent head pose estimation based on random forest regression. In ICIP.
  12. McFarlane, D. (2002). Comparison of four primary methods for coordinating the interruption of people in human-computer interaction. In Human-Computer Interaction.
  13. Minka, T. (1999). The dirichlet-tree distribution. In http://research.microsoft.com/minka/papers/dirichlet/ minkadirtree.pdf.
  14. Murphy-Chutorian, E. and Trivedi, M. (2009). Head pose estimation in computer vision: A survey. In Transactions on Pattern Analysis and Machine Intelligence.
  15. Shotton, J. and Fitzgibbon, A. (2011). Real-time human pose recognition in parts from single depth images. In CVPR.
  16. Sun, M. and Kohli, P. (2012). Conditional regression forests for human pose estimation. In CVPR.
  17. Yan, X. and Han, C. (2011). Mutiple target tracking by probability hypothesis density based on dirichlet distribution. In Journal of XiAn JiaoTong University.
Download


Paper Citation


in Harvard Style

Liu Y., Chen J., Liu L., Gong Y. and Luo N. (2014). Dirichlet-tree Distribution Enhanced Random Forests for Head Pose Estimation . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 87-95. DOI: 10.5220/0004825000870095


in Bibtex Style

@conference{icpram14,
author={Yuanyuan Liu and Jingying Chen and Leyuan Liu and Yujiao Gong and Nan Luo},
title={Dirichlet-tree Distribution Enhanced Random Forests for Head Pose Estimation},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={87-95},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004825000870095},
isbn={978-989-758-018-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Dirichlet-tree Distribution Enhanced Random Forests for Head Pose Estimation
SN - 978-989-758-018-5
AU - Liu Y.
AU - Chen J.
AU - Liu L.
AU - Gong Y.
AU - Luo N.
PY - 2014
SP - 87
EP - 95
DO - 10.5220/0004825000870095