A Real-Time Head Pose Estimation Using Adaptive POSIT Based on Modified Supervised Descent Method

  • Zhong-Qiu ZhaoEmail author
  • Kewen Cheng
  • Qinmu Peng
  • Xindong Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9771)


In this paper, we proposed a real-time head pose estimation algorithm by extending Pose from Orthography and Scaling with Iterations (POSIT) (named Adaptive POSIT) method and modifying the Supervised Descent Method (SDM). Specifically, we used the modified SDM for facial landmarks detection and tracking, and adopted adaptive POSIT to estimate head pose. In the feature selection stage, we extracted different features in neighboring facial landmarks instead of a single feature. In the facial landmarks selection stage, we used partial facial landmarks instead of the whole facial landmarks. The experiments show that our method can track facial landmarks robustly with tolerance to certain illumination changes and partial occlusion, and improves the accuracy of head pose estimation.


Head pose estimation SDM POSIT Facial landmarks 


  1. 1.
    Tu, J., Huang, T., Xiong, Y.: Calibrating head pose estimation in videos for meeting room event analysis. In: ICIP, pp. 3193–3196 (2006)Google Scholar
  2. 2.
    DeMenthon, D.F., Davis, L.S.: Model-based object pose in 25 lines of code. Int. J. Comput. Vis. 15, 123–141 (1995)CrossRefGoogle Scholar
  3. 3.
    Xiong, X.H., Fernando, D.L.T.: Supervised descent method and its applications to face alignment. In: CVPR, pp. 532–539 (2013)Google Scholar
  4. 4.
    Lablack, A., Zhang, Z., Djeraba, C.: Supervised learning for head pose estimation using SVD and gabor wavelets, pp. 592–596. IEEE (2008)Google Scholar
  5. 5.
    Voit, M., Nickel, K., Stiefelhagen, R.: Multi-view head pose estimation using neural networks. In: International Conference on Computer and Robot Vision, IEEE Computer Society, pp. 347–352 (2005)Google Scholar
  6. 6.
    Xin, G., Yu, X.: Head pose estimation based on multivariate label distribution. In: CVPR, pp. 1837–1842 (2014)Google Scholar
  7. 7.
    Fanelli, G., Gall, J., Van, Gool, L.: Real time head pose estimation with random regression forests. In: CVPR, pp. 617–624 (2011)Google Scholar
  8. 8.
    Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active shape models-their training and application. Comput. Vis. Image Underst. 61(1), 38–59 (1995)CrossRefGoogle Scholar
  9. 9.
    Cootes, T., Edwards, G., Taylor, C.: Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 681–685 (2001)CrossRefGoogle Scholar
  10. 10.
    Cristinacce, D., Cootes, T.F.: Feature detection and tracking with constrained local models. In: BMVC (2006)Google Scholar
  11. 11.
    Cao, X., Wei, Y., Wen, F., et al.: Face alignment by explicit shape regression. Int. J. Comput. Vis. 107(2), 2887–2894 (2014)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Ren, S., Cao, X., Wei, Y., et al.: Face alignment at 3000 FPS via regressing local binary features. In: CVPR, pp. 1685–1692 (2014)Google Scholar
  13. 13.
    Chen, D., Ren, S., Wei, Y., Cao, X., Sun, J.: Joint cascade face detection and alignment. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part VI. LNCS, vol. 8694, pp. 109–122. Springer, Heidelberg (2014)Google Scholar
  14. 14.
    Lowe, D.: Distinctive image features from scale-invariant key points. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  15. 15.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 886–893 (2005)Google Scholar
  16. 16.
    Ahonen, T., Hadid, A., Pietikäinen, M.: Face Recognition with Local Binary Patterns. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  17. 17.
    Felzenszwalb, P.F., Girshick, R.B., McAllester, D.A., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)CrossRefGoogle Scholar
  18. 18.
    Saragih, J.: Principal regression analysis. In: CVPR, pp. 2, 3, 5, 6 (2011)Google Scholar
  19. 19.
    Le, V., Brandt, J., Lin, Z., Bourdev, L., Huang, T.S.: Interactive facial feature localization. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part III. LNCS, vol. 7574, pp. 679–692. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  20. 20.
    Belhumeur, P.N., Jacobs, D.W., Kriegman, D.J., Kumar, N.: Localizing parts of faces using a consensus of exemplars. In: CVPR, pp. 2, 3, 5, 6 (2011)Google Scholar
  21. 21.
    Bradski, G.: The OpenCV library. Dr. Dobbs J. Softw. Tools 25, 120–126 (2000)Google Scholar
  22. 22.
    La Cascia, M., Sclaroff, S., Athitsos, V.: Fast, reliable head tracking under varying illumination: an approach based on registration of texture-mapped 3D models. IEEE Trans. Pattern Anal. Mach. Intell. 22(4), 322–336 (2000)CrossRefGoogle Scholar
  23. 23.
    Tu, J., Huang, T., Tao, H.: Accurate head pose tracking in low resolution video. In: AFGR, pp. 573–578 (2006)Google Scholar
  24. 24.
    An, K.H., Chung, M.: 3D head tracking and pose-robust 2D texture map-based face recognition using a simple ellipsoid model. In: Proceedings Intelligent Robots System, pp. 307–312 (2008)Google Scholar
  25. 25.
    Roberto, V., Nicu, S., Theo, G.: Combining head pose and eye location information for gaze estimation. IEEE Trans. Image Process. 21(2), 802–815 (2012)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Gritti, T.: Toward fully automated face pose estimation. In: Proceedings of the 1st International Workshop on Interactive Multimedia for Consumer Electronics, pp. 79–88. ACM (2009)Google Scholar
  27. 27.
    Kim, W.W., Park, S., Hwang, J., et al.: Automatic head pose estimation from a single camera using projective geometry. In: 2011 8th International Conference on Information Communications and Signal Processing (ICICS), pp. 1–5. IEEE (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Zhong-Qiu Zhao
    • 1
    Email author
  • Kewen Cheng
    • 1
  • Qinmu Peng
    • 2
  • Xindong Wu
    • 1
  1. 1.College of Computer and InformationHefei University of TechnologyHefeiChina
  2. 2.Department of Computer ScienceHong Kong Baptist UniversityHong KongChina

Personalised recommendations