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Mathematical Problems in Engineering
Volume 2015, Article ID 703514, 11 pages
http://dx.doi.org/10.1155/2015/703514
Research Article

Head Pose Estimation with Improved Random Regression Forests

State Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University Chengdu, Sichuan 610064, China

Received 20 May 2015; Accepted 30 September 2015

Academic Editor: Panos Liatsis

Copyright © 2015 Gaoli Sang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. E. Murphy-Chutorian and M. M. Trivedi, “Head pose estimation in computer vision: a survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 4, pp. 607–626, 2009. View at Publisher · View at Google Scholar · View at Scopus
  2. Y. Cai, M. Yang, and Z. Li, “Robust head pose estimation using a 3D morphable model,” Mathematical Problems in Engineering, vol. 2015, Article ID 678973, 10 pages, 2015. View at Publisher · View at Google Scholar
  3. V. Blanz and T. Vetter, “Face recognition based on fitting a 3D morphable model,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 9, pp. 1063–1074, 2003. View at Publisher · View at Google Scholar · View at Scopus
  4. J. Aghajanian and S. J. D. Prince, “Face pose estimation in uncontrolled environments,” in Proceedings of the 20th British Machine Vision Conference (BMVC '09), vol. 1, pp. 1–11, September 2009. View at Publisher · View at Google Scholar · View at Scopus
  5. X. Zhu and D. Ramanan, “Face detection, pose estimation, and landmark localization in the wild,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '12), pp. 2879–2886, IEEE, Providence, RI, USA, June 2012. View at Publisher · View at Google Scholar · View at Scopus
  6. B. Ma, R. Huang, and L. Qin, “VoD: a novel image representation for head yaw estimation,” Neurocomputing, vol. 148, pp. 455–466, 2015. View at Publisher · View at Google Scholar · View at Scopus
  7. R. Zhu, G. Sang, Y. Cai et al., “Head pose estimation with improved random regression forests,” in Proceedings of the 8th Chinese Conference on Biometric Recognition (CCBR '13), pp. 457–465, Jinan, China, November 2013.
  8. M. Fenzi, L. Leal-Taixe, B. Rosenhahn, and J. Ostermann, “Class generative models based on feature regression for pose estimation of object categories,” in Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '13), pp. 755–762, Portland, Ore, USA, June 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. X. Geng and Y. Xia, “Head pose estimation based on multivariate label distribution,” in Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '14), pp. 1837–1842, IEEE, Columbus, Ohio, USA, June 2014. View at Publisher · View at Google Scholar · View at Scopus
  10. D. Amaratunga, J. Cabrera, and Y.-S. Lee, “Enriched random forests,” Bioinformatics, vol. 24, no. 18, pp. 2010–2014, 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. B. Xu, J. Z. Huang, G. Williams, Q. Wang, and Y. Ye, “Classifying very high-dimensional data with random forests built from small subspaces,” International Journal of Data Warehousing and Mining, vol. 8, no. 2, pp. 44–63, 2012. View at Publisher · View at Google Scholar · View at Scopus
  12. H. Kim, H. Kim, H. Moon, and H. Ahn, “A weight-adjusted voting algorithm for ensembles of classifiers,” Journal of the Korean Statistical Society, vol. 40, no. 4, pp. 437–449, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  13. M. Robnik-Šikonja, “Improving random forests,” in Machine Learning: ECML 2004: 15th European Conference on Machine Learning, Pisa, Italy, September 20–24, 2004. Proceedings, J.-F. Boulicaut, F. Esposito, F. Giannotti, and D. Pedreschi, Eds., vol. 3201 of Lecture Notes in Computer Science, pp. 359–370, Springer, Berlin, Germany, 2014. View at Publisher · View at Google Scholar
  14. H. B. Li, W. Wang, H. W. Ding, and J. Dong, “Trees Weighting Random Forest method for classifying high-dimensional noisy data,” in Proceedings of the IEEE International Conference on E-Business Engineering (ICEBE '10), pp. 160–163, Shanghai, China, November 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. P. O. Gislason, J. A. Benediktsson, and J. R. Sveinsson, “Random forests for land cover classification,” Pattern Recognition Letters, vol. 27, no. 4, pp. 294–300, 2006. View at Publisher · View at Google Scholar · View at Scopus
  16. A. Liaw and M. Wiener, “Classification and regression by random forest,” R News, vol. 2, no. 3, pp. 18–22, 2002. View at Google Scholar
  17. H. Pang, A. Lin, M. Holford et al., “Pathway analysis using random forests classification and regression,” Bioinformatics, vol. 22, no. 16, pp. 2028–2036, 2006. View at Publisher · View at Google Scholar · View at Scopus
  18. A. Bosch, A. Zisserman, and X. Muñoz, “Image classification using random forests and ferns,” in Proceedings of the IEEE 11th International Conference on Computer Vision (ICCV '07), pp. 1–8, IEEE, Rio de Janeiro, Brazil, October 2007. View at Publisher · View at Google Scholar · View at Scopus
  19. S. Bernard, S. Adam, and L. Heutte, “Using random forests for handwritten digit recognition,” in Proceedings of the 9th IEEE International Conference on Document Analysis and Recognition (ICDAR '07), pp. 1043–1047, Curitiba, Brazil, September 2007.
  20. R. Khan, A. Hanbury, and J. Stoettinger, “Skin detection: a random forest approach,” in Proceedings of the 17th IEEE International Conference on Image Processing (ICIP '10), pp. 4613–4616, IEEE, Hong Kong, China, September 2010. View at Publisher · View at Google Scholar · View at Scopus
  21. L. Zhang and P. N. Suganthan, “Random forests with ensemble of feature spaces,” Pattern Recognition, vol. 47, no. 10, pp. 3429–3437, 2014. View at Publisher · View at Google Scholar · View at Scopus
  22. C. Huang, X. Ding, and C. Fang, “Head pose estimation based on random forests for multiclass classification,” in Proceedings of the 20th International Conference on Pattern Recognition (ICPR '10), pp. 934–937, Istanbul, Turkey, August 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. Y. Li, S. Wang, and X. Ding, “Person-independent head pose estimation based on random forest regression,” in Proceedings of the 17th IEEE International Conference on Image Processing (ICIP '10), pp. 1521–1524, Hong Kong, September 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. G. Fanelli, M. Dantone, J. Gall, A. Fossati, and L. Van Gool, “Random forests for real time 3D face analysis,” International Journal of Computer Vision, vol. 101, no. 3, pp. 437–458, 2013. View at Publisher · View at Google Scholar · View at Scopus
  25. G. Fanelli, J. Gall, and L. Van Gool, “Real time head pose estimation with random regression forests,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '11), pp. 617–624, IEEE, Providence, RI, USA, June 2011. View at Publisher · View at Google Scholar · View at Scopus
  26. S. Liao, A. K. Jain, and S. Z. Li, “Unconstrained face detection,” Tech. Rep. MSU-CSE-12-15, Department of Computer Science, Michigan State University, East Lansing, Mich, USA, 2012. View at Google Scholar
  27. Z.-H. Shen, Y.-H. Pan, and S.-T. Wang, “A supervised locality preserving projection algorithm for dimensionality reduction,” Pattern Recognition and Artificial Intelligence, vol. 21, no. 2, pp. 233–239, 2008. View at Google Scholar · View at Scopus
  28. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. View at Publisher · View at Google Scholar · View at Scopus
  29. Pointing'04 database, http://www-prima.inrialpes.fr/perso/Gourier/Faces/HPDatabase.html.
  30. W. Gao, B. Cao, S. Shan et al., “The CAS-PEAL large-scale chinese face database and baseline evaluations,” IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, vol. 38, no. 1, pp. 149–161, 2008. View at Publisher · View at Google Scholar · View at Scopus
  31. The National Cheng Kung University face database, http://www.datatang.com/data/14866.
  32. J. A. Black Jr., M. Gargesha, K. Kahol, P. Kuchi, and S. Panchanathan, “A framework for performance evaluation of face recognition algorithms,” in Proceedings of the International Conference on Information Technologies and Communications (ICITC '02), pp. 163–174, 2002.
  33. G. Little, S. Krishna, J. Black, and S. Panchanathan, “A methodology for evaluating robustness of face recognition algorithms with respect to variations in pose angle and illumination angle,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '05), pp. II89–II92, March 2005. View at Publisher · View at Google Scholar · View at Scopus
  34. M. A. Haj, J. Gonzalez, and L. S. Davis, “On partial least squares in head pose estimation: how to simultaneously deal with misalignment,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '12), pp. 2602–2609, IEEE, Providence, RI, USA, June 2012. View at Publisher · View at Google Scholar · View at Scopus
  35. B. Ma, X. Chai, and T. Wang, “A novel feature descriptor based on biologically inspired feature for head pose estimation,” Neurocomputing, vol. 115, pp. 1–10, 2013. View at Publisher · View at Google Scholar · View at Scopus
  36. V. Jain and J. L. Crowley, “Head pose estimation using multi-scale gaussian derivatives,” in Image Analysis: 18th Scandinavian Conference, SCIA 2013, Espoo, Finland, June 17–20, 2013. Proceedings, vol. 7944 of Lecture Notes in Computer Science, pp. 319–328, Springer, Berlin, Germany, 2013. View at Publisher · View at Google Scholar
  37. K. L. Lunetta, L. B. Hayward, J. Segal, and P. van Eerdewegh, “Screening large-scale association study data: exploiting interactions using random forests,” BMC Genetics, vol. 5, no. 1, article 32, 2004. View at Publisher · View at Google Scholar · View at Scopus
  38. C. Strobl and A. Zeileis, “Danger: high power!—exploring the statistical properties of a test for random forest variable importance,” Tech. Rep. 017, University of Munich, Munich, Germany, 2008. View at Google Scholar