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An Integrated Two-Stage Framework for Robust Head Pose Estimation

  • Junwen Wu
  • Mohan M. Trivedi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3723)

Abstract

Subspace analysis has been widely used for head pose estimation. However, such techniques are usually sensitive to data alignment and background noise. In this paper a two-stage approach is proposed to address this issue by combining the subspace analysis together with the topography method. The first stage is based on the subspace analysis of Gabor wavelets responses. Different subspace techniques were compared for better exploring the underlying data structure. Nearest prototype matching using Euclidean distance was used to get the pose estimate. The single pose estimated was relaxed to a subset of poses around it to incorporate certain tolerance to data alignment and background noise. In the second stage, the uncertainty is eliminated by analyzing finer geometrical structure details captured by bunch graphs. This coarse-to-fine framework was evaluated with a large data set. We examined 86 poses, with the pan angle spanning from –90o to 90o and the tilt angle spanning from –60o to 45o. The experimental results indicate that the integrated approach has a remarkably better performance than using subspace analysis alone.

Keywords

Gabor Wavelet Subspace Projection Automatic Face Subspace Analysis Underlie Data Structure 
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.

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References

  1. 1.
    Pappu, R., Beardsley, P.A.: Qualitative approach to classifying gaze direction. In: Proceedings of the IEEE Conf. on Automatic Face and Gesture Recognition (1998)Google Scholar
  2. 2.
    Stiefelhagen, R.: Tracking focus of attention in meetings. In: Proceedings of the IEEE International Conference on Multimodal Interfaces, ICMI 2002 (2002)Google Scholar
  3. 3.
    Huang, K., Trivedi, M.M., Gandhi, T.: Driver’s View and Vehicle Surround Estimation using Omnidirectional Video Stream. In: Proceedings of IEEE Intelligent Vehicles Symposium, Columbus, OH, June 9-11, pp. 444–449 (2003)Google Scholar
  4. 4.
    Braathen, B., Bartlett, M.S., Movellan, J.R.: 3-d head pose estimation from video by stochastic particle filtering. In: Proceedings of the 8th Annual Joint Symposium on Neural Computation (2001)Google Scholar
  5. 5.
    Li, Y., Gong, S., Liddell, H.: Support vector regression and classification based multi-view face detection and recognition. In: Proceeding of IEEE International Conference on Automatic Face and Gesture Recognition, pp. 300–305 (July 2000)Google Scholar
  6. 6.
    Li, S.Z., Fu, Q.D., Gu, L., Scholkopf, B., Cheng, Y.M., Zhang, H.J.: Kernel machine based learning for multi-view face detection and pose estimation. In: Proceedings of 8th IEEE International Conference on Computer Vision (July 2001)Google Scholar
  7. 7.
    Cordea, M., Petriu, E., Georganas, N., Petriu, D., Whalen, T.: Real-time 2.5d head pose recovery for model-based video-coding. In: Proceedings of the IEEE Instrumentation and Measurement Technology Conference (2000)Google Scholar
  8. 8.
    Horprasert, T., Yacoob, Y., Davis, L.S.: An anthropometric shape model for estimating head orientation. In: Proceedings of the 3rd International Workshop on Visual Form (1997)Google Scholar
  9. 9.
    Morency, L., Sundberg, P., Darrell, T.: Pose estimation using 3d view-based eigenspaces. In: Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures, in Conjunction with ICCV 2003, pp. 45–52 (2003)Google Scholar
  10. 10.
    Seemann, E., Nickel, K., Stiefelhagen, R.: Head pose estimation using stereo vision for human-robot interaction. In: Proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition (2004)Google Scholar
  11. 11.
    Chen, L., Zhang, L., Hu, Y., Li, M., Zhang, H.: Head pose estimation using fisher manifold learning. In: Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures, in Conjunction with ICCV 2003 (2003)Google Scholar
  12. 12.
    Gong, S., Sherrah, J., Ong, E.: Understanding pose discrimination in similarity space. In: Proceedings of the The Eleventh British Machine Vision Conference, BMVC 1999 (1999)Google Scholar
  13. 13.
    Wei, Y., Fradet, L., Tan, T.: Head pose estimation using gabor eigenspace modeling. In: Proceedings of the IEEE International Conference on Image Processing (ICIP 2002), vol. 1, pp. 281–284 (2002)Google Scholar
  14. 14.
    Srinivasan, S., Boyer, K.L.: Head pose estimation using view based eigenspaces. In: Proceedings of the 16th International Conference on Pattern Recognition, vol. 4, pp. 302–305 (2002)Google Scholar
  15. 15.
    Potzsch, M., Kruger, N., von der Malsburg, C.: Determination of face position and pose with a learned representation based on labeled graphs. Technical report, Institute for Neuroinformatik, RuhrUniversitat, Bochum, Internal Report (1996)Google Scholar
  16. 16.
    Krüger, V., Sommer, G.: Efficient head pose estimation with gabor wavelet networks. In: Proceedings of the The Eleventh British Machine Vision Conference, BMVC 2000 (2000)Google Scholar
  17. 17.
    Wiskott, L., Fellous, J., Krüger, N., von der Malsburg, C.: Face recognition by elastic bunch graph matching. In: Sommer, G., Daniilidis, K., Pauli, J. (eds.) CAIP 1997. LNCS, vol. 1296, Springer, Heidelberg (1997)Google Scholar
  18. 18.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley-interscience, HobokenGoogle Scholar
  19. 19.
    Scholkopf, B., Smola, A., Muller, K.-R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10, 1299–1319 (1998)CrossRefGoogle Scholar
  20. 20.
    Li, Y., Gong, S., Liddell, H.: Recognising trajectories of facial identities using kernel discriminant analysis. In: Proceedings of the British Machine Vision Conference (BMVC 2001), pp. 613–622 (2001)Google Scholar
  21. 21.
    Mika, S., Ratsch, G., Weston, J., Scholkopf, B., Muller, K.: Fisher discriminant analysis with kernels. In: Proceedings of the IEEE Neural Networks for Signal Processing Workshop, pp. 41–48 (1999)Google Scholar
  22. 22.
    Mac Lennan, J.: Gabor representations ofspatiotemporal visual images. Technical report, Computer Science Department, University of Tennessee, Knoxville, CS-91-144 (1991); Accessible via: http://www.cs.utk.edu/~mclennan
  23. 23.
    Ham, J., Lee, D.D., Mika, S., Scholkopf, B.: A kernel view of dimensionality reduction of manifolds. In: Proceedings of the International Conference on Machine Learning (2004)Google Scholar
  24. 24.
    Viola, P., Jones, M.: Robust Real-time Object Detection. In: Proceedings of the Second International Workshop on Statistical and Computational Theories of Vision - Modeling, Learning and Sampling. Jointed with ICCV 2001 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Junwen Wu
    • 1
  • Mohan M. Trivedi
    • 1
  1. 1.Computer Vision and Robotics Research LaboratoryUniversity of CaliforniaSan Diego, La JollaUSA

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