Advertisement

A Monte Carlo Strategy to Integrate Detection and Model-Based Face Analysis

  • Sandro Schönborn
  • Andreas Forster
  • Bernhard Egger
  • Thomas Vetter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8142)

Abstract

We present a novel probabilistic approach for fitting a statistical model to an image. A 3D Morphable Model (3DMM) of faces is interpreted as a generative (Top-Down) Bayesian model. Random Forests are used as noisy detectors (Bottom-Up) for the face and facial landmark positions. The Top-Down and Bottom-Up parts are then combined using a Data-Driven Markov Chain Monte Carlo Method (DDMCMC). As core of the integration, we use the Metropolis-Hastings algorithm which has two main advantages. First, the algorithm can handle unreliable detections and therefore does not need the detectors to take an early and possible wrong hard decision before fitting. Second, it is open for integration of various cues to guide the fitting process. Based on the proposed approach, we implemented a completely automatic, pose and illumination invariant face recognition application. We are able to train and test the building blocks of our application on different databases. The system is evaluated on the Multi-PIE database and reaches state of the art performance.

Keywords

Face Recognition Face Detection Proposal Distribution Monte Carlo Integration Random Forest Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Andriluka, M., Roth, S., Schiele, B.: Pictorial structures revisited: People detection and articulated pose estimation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1014–1021. IEEE (2009)Google Scholar
  2. 2.
    Belhumeur, P.N., Jacobs, D.W., Kriegman, D.J., Kumar, N.: Localizing parts of faces using a consensus of exemplars. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2011), pp. 545–552. IEEE (2011)Google Scholar
  3. 3.
    Blanz, V., Vetter, T.: A morphable model for the synthesis of 3d faces. In: SIGGRAPH 1999 Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, pp. 187–194. ACM Press (1999)Google Scholar
  4. 4.
    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
  5. 5.
    Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)Google Scholar
  6. 6.
    Dantone, M., Gall, J., Fanelli, G., Van Gool, L.: Real-time facial feature detection using conditional regression forests. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2012), pp. 2578–2585. IEEE (2012)Google Scholar
  7. 7.
    Eckhardt, M., Fasel, I., Movellan, J.: Towards practical facial feature detection. International Journal of Pattern Recognition and Artificial Intelligence 23(03), 379–400 (2009)CrossRefGoogle Scholar
  8. 8.
    Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (voc) challenge. International Journal of Computer Vision 88(2), 303–338 (2010)CrossRefGoogle Scholar
  9. 9.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Pictorial structures for object recognition. International Journal of Computer Vision 61(1), 55–79 (2005)CrossRefGoogle Scholar
  10. 10.
    Forsyth, D.A., Haddon, J., Ioffe, S.: The joy of sampling. International Journal of Computer Vision 41(1-2), 109–134 (2001)CrossRefzbMATHGoogle Scholar
  11. 11.
    Grenander, U.: Lectures in pattern theory. Applied Mathematical Sciences (1976)Google Scholar
  12. 12.
    Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: Multi-pie. Image and Vision Computing 28(5), 807–813 (2010)CrossRefGoogle Scholar
  13. 13.
    Jones, M., Viola, P.: Fast multi-view face detection. Mitsubishi Electric Research Lab TR-20003-96 3 (2003)Google Scholar
  14. 14.
    Köstinger, M., Wohlhart, P., Roth, P.M., Bischof, H.: Annotated facial landmarks in the wild: A large-scale, real-world database for facial landmark localization. In: IEEE International Conference on Computer Vision Workshops (ICCV Workshops 2011), pp. 2144–2151. IEEE (2011)Google Scholar
  15. 15.
    Liu, C., Shum, H.-Y., Zhang, C.: Hierarchical shape modeling for automatic face localization. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part II. LNCS, vol. 2351, pp. 687–703. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  16. 16.
    Paysan, P., Knothe, R., Amberg, B., Romdhani, S., Vetter, T.: 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), pp. 296–301. IEEE (2009)Google Scholar
  17. 17.
    Prabhu, U., Heo, J., Savvides, M.: Unconstrained pose-invariant face recognition using 3d generic elastic models. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(10), 1952–1961 (2011)CrossRefGoogle Scholar
  18. 18.
    Ramamoorthi, R., Hanrahan, P.: An efficient representation for irradiance environment maps. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, pp. 497–500. ACM Press (2001)Google Scholar
  19. 19.
    Rauschert, I., Collins, R.T.: A generative model for simultaneous estimation of human body shape and pixel-level segmentation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part V. LNCS, vol. 7576, pp. 704–717. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  20. 20.
    Robert, C.P., Casella, G.: Monte Carlo statistical methods, vol. 319. Citeseer (2004)Google Scholar
  21. 21.
    Tu, Z., Chen, X., Yuille, A.L., Zhu, S.C.: Image parsing: Unifying segmentation, detection, and recognition. International Journal of Computer Vision 63(2), 113–140 (2005)CrossRefGoogle Scholar
  22. 22.
    Wojek, C., Roth, S., Schindler, K., Schiele, B.: Monocular 3D scene modeling and inference: Understanding multi-object traffic scenes. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 467–481. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  23. 23.
    Zhang, C., Zhang, Z.: A survey of recent advances in face detection. Tech. rep., Microsoft Research (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sandro Schönborn
    • 1
  • Andreas Forster
    • 1
  • Bernhard Egger
    • 1
  • Thomas Vetter
    • 1
  1. 1.Department for Mathematics and Computer ScienceUniversity of BaselSwitzerland

Personalised recommendations