Multiple Faces Tracking Using Motion Prediction and IPCA in Particle Filters

  • Sukwon Choi
  • Daijin Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)


We propose an efficient real-time face tracking system that can track fast moving face and cope with the illumination changes. To achieve these goals, we use the active appearance model(AAM) to represent the face image due to its simplicity and flexibility and take the particle filter framework to track the face image due to its robustness. We modify the particle filter framework as follows. To track fast moving face, we predict the motions using motion history and motion estimation, hence we can reduce the required number of particles. For observation model, we use active appearance model(AAM) to obtain an accurate face region, and update the model using incremental principle component analysis(IPCA). Occlusion handling scheme incorporates motion history to handle the moving face with occlusion. We have expanded our application to multiple faces tracking system. Experimental results present the robustness and effectiveness of the proposed system.


  1. 1.
    Azarbayejani, A., Pentland, A.: Recursive estimation of motion, structure and focal length. IEEE Transactions on Pattern Analysis and Machine Intelligence 17, 562–575 (1995)CrossRefGoogle Scholar
  2. 2.
    Doucet, A., Godsill, S.J., Andrieu, C.: On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing 10(3), 197–209 (2000)CrossRefGoogle Scholar
  3. 3.
    Arulampalam, S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing 50(2), 174–189 (2002)CrossRefGoogle Scholar
  4. 4.
    Okuma, K., Taleghani, A., de Freitas, N., Little, J., Lowe, D.: A boosted particle filter: Multitarget detection and tracking. In: Proceddings of European Conference on Computer Vision, pp. 28–39 (2004)Google Scholar
  5. 5.
    Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1), 72–86 (1991)CrossRefGoogle Scholar
  6. 6.
    Jepson, A.D., Fleet, D.J., El-Maraghi, T.: Robust online appearance model for visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(10), 1296–1311 (2003)CrossRefGoogle Scholar
  7. 7.
    Zhou, S., Chellappa, R., Moghaddam, B.: Visual tracking and recognition using appearance-adaptive models in particle filters. IEEE Transactions on Image Processing 13(11), 1491–1506 (2004)CrossRefGoogle Scholar
  8. 8.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: Proceedings of 5th European Conference on Computer Vision, pp. 484–498 (1998)Google Scholar
  9. 9.
    Hamlaoui, S., Davoine, F.: Facial action tracking using particle filters and active appearance models. In: Joint sOc-EUSAI conference, pp. 165–169 (2005)Google Scholar
  10. 10.
    Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of the 7th International Joint Conference on Artificial Intelligence, pp. 674–679 (1981)Google Scholar
  11. 11.
    Hall, P., Marshall, D., Martin, R.: Incremental eigenanalysis for classification. In: Proceedings of British Machine Vision Conference, pp. 286–295 (1998)Google Scholar
  12. 12.
    Artac, M., Jogan, M., Leonardis, A.: Incremental PCA for on-line visual learning and recognition. In: International Conference on Pattern Recognition, pp. 781–784 (2002)Google Scholar
  13. 13.
    Ross, D.A., Lim, J., Yang, M.-H.: Adaptive probabilistic visual tracking with incremental subspace update. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3022, pp. 470–482. Springer, Heidelberg (2004)Google Scholar
  14. 14.
    Huber, P.J.: Robust statistics. John Wiley, Chichester (1982)Google Scholar
  15. 15.
    Bhaskaran, V., Konstantinides, K.: Image and video compression standards. Kluwer Academic Publishers, Dordrecht (1997)Google Scholar
  16. 16.
    Froba, B., Ernst, A.: Face detection with the modified census transform. In: Sixth IEEE International Conference on Automatic Face and Gesture Recognition, IEEE Computer Society Press, Los Alamitos (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Sukwon Choi
    • 1
  • Daijin Kim
    • 1
  1. 1.Intelligent Multimedia Laboratory, Dept. of Computer Science & Engineering, Pohang University of Science and Technology (POSTECH), PohangKorea

Personalised recommendations