Abstract
In this paper, we propose a supervised Smooth Multi-Manifold Embedding (SMME) method for robust identity-independent head pose estimation. In order to handle the appearance variations caused by identity, we consider the pose data space as multiple manifolds in which each manifold characterizes the underlying subspace of subjects with similar appearance. We then propose a novel embedding criterion to learn each manifold from the exemplar-centered local structure of subjects. The experiment results on the standard databases demonstrates that the SMME is robust to variations of identities and achieves high pose estimation accuracy.
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References
Murphy-Chutorian, E., Trivedi, M.: Head pose estimation in computer vision: a survey. IEEE Transactions on PAMI, 442–449 (2008)
Sebastian, H., Lee, D.: The manifold ways of perception. Science 290(12), 2268–2269 (2000)
Tenenbaum, J., Silva, V., Langford, J.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)
Fu, Y., Huang, T.: Graph embedded analysis for head pose estimation. In: Proc. of International Conference on Automatic Face and Gesture Recognition (2006)
Wang, X., Huang, X., Gao, J., Yang, R.: Illumination and person-insensitive head pose estimation using distance metric learning. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 624–637. Springer, Heidelberg (2008)
Raytchev, B., Yoda, I., Sakaue, K.: Head pose estimation by nonlinear manifold learning. In: ICPR (2004)
Balasubramanian, V., Ye, J., Panchanathan, S.: Biased manifold embedding: a framework for person-independent head pose estimation. In: CVPR (2007)
Yan, S., Wang, H., Fu, Y., Yan, J., Tang, X., Huang, T.S.: Synchronized submanifold embedding for person-independent pose estimation and beyond. IEEE Transactions on Image Processing (2008)
Kim, T., Kittler, J.: Locally linear discriminant analysis for multimodally distributed classes for face recognition with a single model image. IEEE Transactions on PAMI 27(3), 318–327 (2005)
Vidal, R., Ma, Y., Sastry, S.: Generalized principal component analysis (GPCA). IEEE Transactions on PAMI 27(12), 1945–1959 (2005)
Tipping, M., Bishop, C.: Mixtures of probabilistic principal component analyzers. Neural computation 11(2), 443–482 (1999)
Frey, B., Dueck, D.: Clustering by passing messages between data points. Science 315(514), 972–977 (2007)
Roweis, S., Saul, L.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)
Little, D., Krishna, S., Black, J., Panchanathan, S.: A methodology for evaluating robustness of face recognition algorithms with respect to variations in pose angle and illumination angle. In: ICASSP, vol. 2 (2005)
Gourier, N., Hall, D., Crowley, J.: Estimating Face orientation from Robust Detection of Salient Facial Structures. In: VODG, pp. 281–290 (2004)
Yan, S., Xu, D., Zhang, B., Zhang, H., Yang, Q., Lin, S.: Graph embedding and extensions: a general framework for dimensionality reduction. PAMI, 40–51 (2007)
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© 2009 Springer-Verlag Berlin Heidelberg
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Liu, X., Lu, H., Luo, H. (2009). Smooth Multi-Manifold Embedding for Robust Identity-Independent Head Pose Estimation. In: Jiang, X., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2009. Lecture Notes in Computer Science, vol 5702. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03767-2_8
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DOI: https://doi.org/10.1007/978-3-642-03767-2_8
Publisher Name: Springer, Berlin, Heidelberg
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