International Conference on Computer Analysis of Images and Patterns

CAIP 2015: Computer Analysis of Images and Patterns pp 254-265 | Cite as

Real-Time Head Pose Estimation Using Multi-variate RVM on Faces in the Wild

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9257)

Abstract

Various computer vision problems and applications rely on an accurate, fast head pose estimator. We model head pose estimation as a regression problem. We show that it is possible to use the appearance of the facial image as a feature which depicts the pose variations. We use a parametrized Multi-Variate Relevance Vector Machine (MVRVM) to learn the three rotation angles of the face (yaw, pitch, and roll). The input of the MVRVM is normalized mean pixel intensities of the face patches, and the output is the three head rotation angles. We evaluated our approach on the challenging YouTube faces dataset. We achieved a head pose estimation with an average error tolerance of \(\pm \)6.5\(^\circ \) in the yaw rotation angle, and less than \(\pm \)2.5\(^\circ \) in both the pitch and roll angles. The time taken in one prediction is 2-3 milliseconds, hence suitable for real-time applications.

Keywords

Head pose estimation Real-time MVRVM YouTube faces 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    Asthana, A., Zafeiriou, S., Cheng, S., Pantic, M.: Incremental face alignment in the wild. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1859–1866. IEEE (2014)Google Scholar
  3. 3.
    Best-Rowden, L., Klare, B., Klontz, J., Jain, A.K.: Video-to-video face matching: establishing a baseline for unconstrained face recognition. In: 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1–8. IEEE (2013)Google Scholar
  4. 4.
    Beveridge, J.R., Phillips, P.J., Bolme, D.S., Draper, B.A., Givens, G.H., Lui, Y.M., Teli, M.N., Zhang, H., Scruggs, W.T., Bowyer, K.W., Flynn, P.J., Cheng, S.: The challenge of face recognition from digital point-and-shoot cameras. In: 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1–8, September 2013Google Scholar
  5. 5.
    Blanz, V., Vetter, T.: Face recognition based on fitting a 3d morphable model. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(9), 1063–1074 (2003)CrossRefGoogle Scholar
  6. 6.
    Cootes, T.F., Wheeler, G.V., Walker, K.N., Taylor, C.J.: View-based active appearance models. Image and Vision Computing 20(9), 657–664 (2002)CrossRefGoogle Scholar
  7. 7.
    Fanelli, G., Gall, J., Van Gool, L.: Real time head pose estimation with random regression forests. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 617–624. IEEE (2011)Google Scholar
  8. 8.
    Gu, L., Kanade, T.: 3d alignment of face in a single image. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 1305–1312. IEEE (2006)Google Scholar
  9. 9.
    Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Technical Report 07–49, University of Massachusetts, Amherst, October 2007Google Scholar
  10. 10.
    Jones, M., Viola, P.: Fast multi-view face detection. Mitsubishi Electric Research Lab TR-20003-96, 3:14 (2003)Google Scholar
  11. 11.
    Kan, M., Xu, D., Shan, S., Li, W., Chen, X.: Learning prototype hyperplanes for face verification in the wild. IEEE Transactions on Image Processing 22(8), 3310–3316 (2013)CrossRefGoogle Scholar
  12. 12.
    Murphy-Chutorian, E., Trivedi, M.M.: Head pose estimation in computer vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(4), 607–626 (2009)CrossRefGoogle Scholar
  13. 13.
    Pentland, A., Moghaddam, B., Starner, T.: View-based and modular eigenspaces for face recognition. In: 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Proceedings CVPR 1994, pp. 84–91. IEEE (1994)Google Scholar
  14. 14.
    Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The feret evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1090–1104 (2000)CrossRefGoogle Scholar
  15. 15.
    Thayananthan, A., Navaratnam, R., Stenger, B., Torr, P.H.S., Cipolla, R.: Multivariate relevance vector machines for tracking. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3953, pp. 124–138. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  16. 16.
    Tipping, M.E.: Sparse bayesian learning and the relevance vector machine. The Journal of Machine Learning Research 1, 211–244 (2001)MATHMathSciNetGoogle Scholar
  17. 17.
    Valenti, R., Sebe, N., Gevers, T.: Combining head pose and eye location information for gaze estimation. IEEE Transactions on Image Processing 21(2), 802–815 (2012)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Wolf, L., Hassner, T., Maoz, I.: Face recognition in unconstrained videos with matched background similarity. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 529–534. IEEE (2011)Google Scholar
  19. 19.
    Zhu, X., Ramanan, D.: Face detection, pose estimation, and landmark localization in the wild. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2879–2886. IEEE (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Augmented Vision Research Group, German Research Center for Artificial Intelligence (DFKI)Technical University of KaiserslauternKaiserslauternGermany

Personalised recommendations