OBJCUT for Face Detection

  • Jonathan Rihan
  • Pushmeet Kohli
  • Philip H. S. Torr
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4338)


This paper proposes a novel, simple and efficient method for face segmentation which works by coupling face detection and segmentation in a single framework. We use the OBJCUT [1] formulation that allows for a smooth combination of object detection and Markov Random Field for segmentation, to produce a real-time face segmentation. It should be noted that our algorithm is extremely efficient and runs in real time.


Image Segmentation Face Detection False Detection Detection Window Shape Prior 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kumar, M.P., Torr, P.H.S., Zisserman, A.: OBJ CUT. In: CVPR, vol. I, pp. 18–25 (2005)Google Scholar
  2. 2.
    Boykov, Y., Jolly, M.: Interactive graph cuts for optimal boundary and region segmentation of objects in n-d images. In: ICCV, vol. I, pp. 105–112 (2001)Google Scholar
  3. 3.
    Bray, M., Kohli, P., Torr, P.: PoseCut: Simulataneous segmentation and 3d pose estimation of humans using dynamic graph cuts. In: ECCV, pp. 642–655 (2006)Google Scholar
  4. 4.
    Freedman, D., Zhang, T.: Interactive graph cut based segmentation with shape priors. In: CVPR, vol. I, pp. 755–762 (2005)Google Scholar
  5. 5.
    Kohli, P., Torr, P.: Efficiently solving dynamic markov random fields using graph cuts. In: ICCV (2005)Google Scholar
  6. 6.
    Rother, C., Kolmogorov, V., Blake, A.: Grabcut: Interactive foreground extraction using iterated graph cuts. ACM Trans. Graph, 309–314 (2004)Google Scholar
  7. 7.
    A,, Criminisi, C.G., A,, Blake, K.V.: Bilayer segmentation of live video. In: IEEE Computer Vision and Pattern Recognition (CVPR) (2006)Google Scholar
  8. 8.
    Sun, I., Zhang, W., Tang, X., Shum, H.: Background cut. In: ECCV (2006)Google Scholar
  9. 9.
    Kolmogorov, V., Criminisi, A., Blake, A., Cross, G., Rother, C.: Bi-layer segmentation of binocular stereo video. In: CVPR (2), pp. 407–414 (2005)Google Scholar
  10. 10.
    Huang, R., Pavlovic, V., Metaxas, D.N.: A graphical model framework for coupling mrfs and deformable models. In: CVPR, vol. II, pp. 739–746 (2004)Google Scholar
  11. 11.
    Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. PAMI 26, 1124–1137 (2004)Google Scholar
  12. 12.
    Greig, D., Porteous, B., Seheult, A.: Exact maximum a posteriori estimation for binary images. Journal of the Royal Statistical Society 2, 271–279 (1989)Google Scholar
  13. 13.
    Kolmogorov, V., Zabih, R.: What energy functions can be minimized via graph cuts? In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, p. 65. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  14. 14.
    Viola, P., Jones, M.: Robust real-time object detection. International Journal of Computer Vision (2004)Google Scholar
  15. 15.
    Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. Computational Learning Theory: Eurocolt 95, 23–37 (1995)Google Scholar
  16. 16.
    Kumar, S., Hebert, M.: Discriminative fields for modeling spatial dependencies in natural images. In: NIPS (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jonathan Rihan
    • 1
  • Pushmeet Kohli
    • 1
  • Philip H. S. Torr
    • 1
  1. 1.Department of ComputingOxford Brookes UniversityUK

Personalised recommendations