Abstract
Precise 3D pose estimation plays a significant role in developing human-computer interfaces and practical face recognition systems. This task is challenging due to the personality in pose variation for a certain subject. In this work, the pose data space is considered as a union of the submanifolds which characterize different subjects, instead of a single continuous manifold as conventionally regarded. A novel manifold embedding algorithm dually supervised by subjects and poses, called Synchronized Submanifold Embedding (SSE), is proposed for person-independent precise pose estimation. First, the submanifold of a certain subject is approximated as a set of simplexes constructed using neighboring samples. Then, these simplexized submanifolds from different subjects are embedded by synchronizing the locally propagated poses within the simplexes and at the same time maximizing the intra-submanifold variances. Finally, the pose of a new datum is estimated as the median of the poses for the nearest neighbors in the dimensionality reduced feature space. The experiments on the 3D pose estimation database, CHIL data for CLEAR07 evaluation demonstrate the effectiveness of our proposed algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ba, S., Dobez, J.: A Probabilistic Framework for Joint Head Tracking and Pose Estimation. In: Proceedings of International Conference on Pattern Recognition, vol. 4, pp. 264–267 (2004)
Belkin, M., Niyogi, P.: Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering. Advances in Neural Information Processing System, 585–591 (2001)
Bregler, C., Omohundro, S.: Nonlinear image interpolation using manifold learning. Advances in Neural Information Processing Systems, 973–980 (1995)
Brown, L., Tian, Y.: Comparative study of coarse head pose estimation. In: Proceedings of IEEE Workshop on Motion and Video Computing, pp. 125–130 (2002)
Chen, L., Zhang, L., Hu, Y., Li, M., Zhang, H.: Head pose estimation using fisher manifold learning. In: Proceedings of IEEE International Workshop on Analysis and Modeling of Faces and Gestures, pp. 203–207 (2003)
Cootes, T., Edwards, G., Taylor, C.: Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(6), 681–685 (2001)
Fu, Y., Huang, T.: Graph embedded analysis for head pose estimation. In: Proceddings of the 7th International Conference on Automatic Face and Gesture Recognition, pp. 3–8 (2006)
Fukunnaga, K.: Introduction to Statistical Pattern Recognition, 2nd edn. Academic Press, London (1991)
He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.: Face Recognition Using Laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(3), 328–340 (2005)
Hu, N., Huang, W., Ranganath, S.: Head pose estimation by non-linear embedding and mapping. In: Proceedings of IEEE International Conference on Image Processing, pp. 342–345 (2005)
Jolliffe, I.: Principal Component Analysis. Springer, Heidelberg (1986)
Lanitis, A., Draganova, C., Christodoulou, C.: Comparing different classifiers for automatic age estimation. IEEE Transactions on Systems, Man and Cybernetics, Part B 34, 621–628 (2004)
Li, S., Fu, Q., Gu, L., Scholkopf, B., Cheng, Y., Zhang, H.: Kernel machine based learning for multi-view face detection and pose estimation. In: Proceedings of the International Conference on Computer Vision, vol. 2, pp. 674–679 (2001)
Li, S., Lu, X., Hou, X., Peng, X., Cheng, Q.: Learning multiview face subspaces and facial pose estimation using independent component analysis. IEEE Transactions on Image Processing 14(6), 705–712 (2005)
Munkres, J.: Elements of Algebraic Topology. Perseus Press (1993)
Raytchev, B., Yoda, I., Sakaue, K.: Head pose estimation by nonlinear manifold learning. In: Proceedings of the 17th International Conference on Pattern Recognition, vol. 4, pp. 1051–4651 (2004)
Ritte, D., Kouropteva, O., Okun, O., Pietikainen, M., Duin, R.: Supervised locally linear embedding. In: Proceedings of Artificial Neural Networks and Neural Information, pp. 333–341 (2003)
Roweis, S., Saul, L.: Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 290(22), 2323–2326 (2000)
Saul, L., Roweis, S.: Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifolds. Journal of Machine Learning Research 4, 119–155 (2003)
Tenenbaum, J., Silva, V., Langford, J.: A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science 290(22), 2319–2323 (2000)
Tu, J., Fu, Y., Hu, Y., Huang, T.: Evaluation of Head Pose Estimation For Studio Data. In: Stiefelhagen, R., Garofolo, J.S. (eds.) CLEAR 2006. LNCS, vol. 4122, pp. 281–290. Springer, Heidelberg (2007)
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 13, 71–86 (1991)
Wang, H., Ahuja, N.: Facial expression decomposition. In: IEEE International Conference on Computer Vision, vol. 2, pp. 958–965 (2003)
Weinberger, K., Saul, L.: Unsupervised Learning of Image Manifolds by Semidefinite Programming. In: Proceddings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 988–995 (2004)
Wenzel, M., Schiffmann, W.: Head pose estimation of partially occluded faces. In: Proceeding of the Second Canadian Conference on Computer and Robot Vision, pp. 353–360 (2005)
Yan, S., Xu, D., Zhang, B., Zhang, H., Yang, Q., Lin, S.: Graph Embedding and Extensions: A General Framework for Dimensionality Reduction. Proc. IEEE Trans. Pattern Analysis and Machine Intelligence 29(1), 40–51 (2007)
Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.: Face Recognition: A Literature Survey. ACM Computing Surveys, 399–458 (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yan, S., Zhang, Z., Fu, Y., Hu, Y., Tu, J., Huang, T. (2008). Learning a Person-Independent Representation for Precise 3D Pose Estimation. In: Stiefelhagen, R., Bowers, R., Fiscus, J. (eds) Multimodal Technologies for Perception of Humans. RT CLEAR 2007 2007. Lecture Notes in Computer Science, vol 4625. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68585-2_28
Download citation
DOI: https://doi.org/10.1007/978-3-540-68585-2_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68584-5
Online ISBN: 978-3-540-68585-2
eBook Packages: Computer ScienceComputer Science (R0)