Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2015, Article ID 678973, 10 pages
http://dx.doi.org/10.1155/2015/678973
Research Article

Robust Head Pose Estimation Using a 3D Morphable Model

1School of Computer Science, Sichuan University, Chengdu 610064, China
2Wisesoft Software Co., Ltd., Chengdu 610045, China
3College of Information Engineering, Sichuan Agricultural University, Ya’an 625014, China
4School of Aeronautics and Astronautics, Sichuan University, Chengdu 610064, China

Received 30 September 2014; Accepted 17 November 2014

Academic Editor: Hui Zhang

Copyright © 2015 Ying Cai 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. J. Tu, T. Huang, and H. Tao, “Accurate head pose tracking in low resolution video,” in Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition (FGR '06), pp. 573–578, April 2006. View at Publisher · View at Google Scholar · View at Scopus
  2. M. Reale, P. Liu, and L. Yin, “Using eye gaze, head pose, and facial expression for personalized non-player character interaction,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW '11), pp. 13–18, Colorado Springs, Colo, USA, June 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. S. O. Ba and J. M. Odobez, “Recognizing visual focus of attention from head pose in natural meetings,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 39, no. 1, pp. 16–33, 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. K. Huang and M. M. Trivedi, “Driver head pose and view estimation with single omnidirectional video stream,” in Proceedings of the 1st International Workshop on In-Vehicle Cognitive Computer Vision Systems and the 3rd International Conference on Computer Vision Systems, Graz, Austria, 2003.
  5. X. Chai, S. Shan, X. Chen, and W. Gao, “Locally linear regression for pose-invariant face recognition,” IEEE Transactions on Image Processing, vol. 16, no. 7, pp. 1716–1725, 2007. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  6. R. Valenti, Z. Yucel, and T. Gevers, “Robustifying eye center localization by head pose cues,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR '09), pp. 612–618, Miami, Fla, USA, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. T. Weise, B. Leibe, and L. van Gool, “Fast 3D scanning with automatic motion compensation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR '07), pp. 1–8, IEEE, June 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. H. Zhang, Y. Shi, and A. S. Mehr, “Robust weighted H filtering for networked systems with intermittent measurements of multiple sensors,” International Journal of Adaptive Control and Signal Processing, vol. 25, no. 4, pp. 313–330, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  9. 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
  10. A. Gee and R. Cipolla, “Determining the gaze of faces in images,” Image and Vision Computing, vol. 12, no. 10, pp. 639–647, 1994. View at Publisher · View at Google Scholar · View at Scopus
  11. J.-G. Wang and E. Sung, “EM enhancement of 3D head pose estimated by point at infinity,” Image and Vision Computing, vol. 25, no. 12, pp. 1864–1874, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. A. Lanitis, C. J. Taylor, T. F. Cootes, and T. Ahmed, “Automatic interpretation of human faces and hand gestures using flexible models,” in Proceedings of the International Workshop on Automatic Face- and Gesture-Recognition, 1995.
  13. X. Wang, X. Huang, J. Gao, and R. Yang, “Illumination and person-insensitive head pose estimation using distance metric learning,” in Computer Vision—ECCV 2008, pp. 624–637, Springer, 2008. View at Google Scholar
  14. Y. Fu and T. S. Huang, “Graph embedded analysis for head pose estimation,” in Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition (FGR '06), pp. 3–8, Southampton, UK, April 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. B. Raytchev, I. Yoda, and K. Sakaue, “Head pose estimation by nonlinear manifold learning,” in Proceedings of the 17th International Conference on Pattern Recognition (ICPR '04), vol. 4, pp. 462–466, IEEE, Cambridge, UK, August 2004.
  16. J. Wu and M. M. Trivedi, “A two-stage head pose estimation framework and evaluation,” Pattern Recognition, vol. 41, no. 3, pp. 1138–1158, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  17. M. Storer, M. Urschler, and H. Bischof, “3D-MAM: 3D morphable appearance model for efficient fine head pose estimation from still images,” in Proceedings of the 12th IEEE International Conference on Computer Vision Workshops (ICCV Workshops '09), pp. 192–199, Kyoto, Japan, October 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. S. Romdhani, Face image analysis using a multiple features fitting strategy [Ph.D. thesis], University of Basel, 2005.
  19. V. Blanz and T. Vetter, “A morphable model for the synthesis of 3D faces,” in Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, pp. 187–194, ACM Press/Addison-Wesley, 1999.
  20. T. Vetter and T. Poggio, “Linear object classes and image synthesis from a single example image,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 733–742, 1997. View at Publisher · View at Google Scholar · View at Scopus
  21. J. R. Tena Rodriguez, 3D face modelling for 2D+ 3D face recognition [Ph.D. thesis], Centre for Vision, Speech, and Signal Processing, University of Surrey, 2007.
  22. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948, Perth, Australia, December 1995. View at Scopus
  23. R. C. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” in Proceedings of the 6th International Symposium on Micro Machine and Human Science, vol. 1, pp. 39–43, New York, NY, USA, 1995.
  24. “MEGVII spotlighting vision technology,” Face data application research page, http://www.faceplusplus.com.cn/.
  25. I. Jolliffe, Principal Component Analysis, John Wiley & Sons, 2005.
  26. L. Sirovich and M. Kirby, “Low-dimensional procedure for the characterization of human faces,” Journal of the Optical Society of America, vol. 4, no. 3, pp. 519–524, 1987. View at Publisher · View at Google Scholar · View at Scopus
  27. P. Paysan, R. Knothe, B. Amberg, S. Romdhani, and T. Vetter, “A 3D face model for pose and illumination invariant face recognition,” in Proceedings of the 6th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS '09), pp. 296–301, Genova, Italy, September 2009. View at Publisher · View at Google Scholar · View at Scopus
  28. E. Angel, Interactive Computer Graphics: A Top-Down Approach Using OpenGL, Addison-Wesley, 2003.
  29. L. Ding, X. Ding, and C. Fang, “L1-constrained 3-d face sparse reconstruction,” Journal of Tsinghua University (Science and Technology), no. 5, pp. 581–585, 2012. View at Google Scholar
  30. H. Zhang and J. Wang, “State estimation of discrete-time Takagi–Sugeno fuzzy systems in a network environment,” IEEE Transactions on Cybernetics, 2014. View at Google Scholar
  31. Y. Ge, 3D face modeling based on morphable model [Ph.D. thesis], Beijing University of Technology, 2012.
  32. X. Gong and G. Wang, “A dynamic component deforming model for face shape reconstruction,” in Advances in Visual Computing, pp. 488–497, Springer, 2007. View at Google Scholar
  33. L. Yin, X. Wei, Y. Sun, J. Wang, and M. J. Rosato, “A 3D facial expression database for facial behavior research,” in Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition (FGR '06), pp. 211–216, IEEE, April 2006. View at Publisher · View at Google Scholar · View at Scopus
  34. J. A. Black Jr., M. Gargesha, K. Kahol, P. Kuchi, and S. Panchanathan, “A framework for performance evaluation of face recognition algorithms,” in Internet Multimedia Management Systems III, vol. 4862 of Proceedings of the SPIE, pp. 163–174, Boston, Mass, USA, August 2002. View at Publisher · View at Google Scholar · View at Scopus
  35. A. Asthana, T. K. Marks, M. J. Jones, K. H. Tieu, and M. V. Rohith, “Fully automatic pose-invariant face recognition via 3D pose normalization,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV '11), pp. 937–944, IEEE, Barcelona, Spain, November 2011. View at Publisher · View at Google Scholar · View at Scopus
  36. T. Sim, S. Baker, and M. Bsat, “The CMU pose, illumination, and expression (PIE) database,” in Proceedings of the 5th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 46–51, Washington, DC, USA, May 2002. View at Publisher · View at Google Scholar
  37. 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