Abstract
In this paper, we propose an embedding method to seek an optimal low-dimensional manifold describing the intrinsical pose variations and to provide an identity-independent head pose estimator. In order to handle the appearance variations caused by identity, we use a learned Mahalanobis distance to seek optimal subjects with similar manifold to construct the embedding. Then, we propose a new smooth and discriminative embedding method supervised by both pose and identity information. To estimate pose of a head new image, we first find its k-nearest neighbors of different subjects, and then embed it into the manifold of the subjects to estimate the pose angle. The empirical study on the standard databases demonstrates that the proposed method 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)
Chai, X., Shan, S., Chen, X., Gao, W.: Locally linear regression for pose-invariant face recognition. IEEE Transactions on Image Processing 16(7), 1716–1725 (2007)
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)
Raytchev, B., Yoda, I., Sakaue, K.: Head pose estimation by nonlinear manifold learning. In: ICPR (2004)
Yan, S., Xu, D., Zhang, B., Zhang, H., Yang, Q., Lin, S.: Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Transactions on PAMI, 40–51 (2007)
Srinivasan, S., Boyer, K.: Head pose estimation using view based eigenspaces. In: ICPR, vol. 16, pp. 302–305 (2002)
Wu, J., Trivedi, M.: A two-stage head pose estimation framework and evaluation. PR 41(3), 1138–1158 (2008)
Balasubramanian, V., Ye, J., Panchanathan, S.: Biased manifold embedding: a framework for person-independent head pose estimation. In: CVPR (2007)
Wang, X., Huang, X., Gao, J., Yang, R.: Illumination and person-insensitive head pose estimation using distance metric learning. In: ECCV, vol. 2, pp. 624–637. IEEE Computer Society, Los Alamitos (2008)
Sugiyama, M.: Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis. Journal of Machine Learning Research 8, 1027–1061 (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)
Liu, X., Lu, H., Luo, H.: Smooth Multi-Manifold Embedding for Robust Identity-Independent Head Pose Estimation. In: Jiang, X., Petkov, N. (eds.) Computer Analysis of Images and Patterns. LNCS, vol. 5702, pp. 66–73. Springer, Heidelberg (2009)
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)
Roweis, S., Saul, L.: Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 290(5500), 2323–2326 (2000)
Bar-Hillel, A., Hertz, T., Shental, N., Weinshall, D.: Learning distance functions using equivalence relations. In: Proc. International conference on Machine learning, vol. 20, p. 11 (2003)
Gourier, N., Hall, D., Crowley, J.: Estimating Face orientation from Robust Detection of Salient Facial Structures. In: Proc. International Workshop on Visual Observation of Deictic Gestures (2004)
Hu, N., Huang, W., Ranganath, S.: Head pose estimation by non-linear embedding and mapping. In: ICIP, vol. 2, pp. 342–345 (2005)
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Liu, X., Lu, H., Zhang, D. (2010). Head Pose Estimation Based on Manifold Embedding and Distance Metric Learning. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12307-8_6
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DOI: https://doi.org/10.1007/978-3-642-12307-8_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12306-1
Online ISBN: 978-3-642-12307-8
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