Head pose estimation using fisher manifold learning

IEEE International Workshop on Analysis and Modeling of Faces and Gestures, AMFG 2003

Volumn , Issue , 2003, Pages 203-207

  Chen, Longbin ^a   Zhang, Lei ^b   Hu, Yuxiao ^b   Li, Mmgjmg ^b   Zhang, Hongjiang ^b  

^a UNIVERSITY OF MIAMI   (United States)

^b MICROSOFT RESEARCH ASIA   (China)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE RECOGNITION;

FACE IMAGES; GENERALIZATION ABILITY; HEAD POSE; HEAD POSE ESTIMATION; LEARNING STRATEGY; MANIFOLD LEARNING; NONLINEAR INTERPOLATION; POSE ESTIMATION ERRORS; REGRESSION METHOD; TRAINING IMAGE;

REGRESSION ANALYSIS;

EID: 85117911447     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. P.N. Belhumeur, J.P.Hespanha, D.J.Kriegman, Eigenfaces vs. Fisherfaces: recognition using class specific linear projection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19 no.7, pp: 711 -720, Jul 1997
2. V.Blanz and T.Vetter, A Morphable Model for the synthesis of 3D Faces, In Proceeding of SIGGRAPH, pp:187-194, 1999
3. S. Birchfield, Elliptical Head Tracking Using Intensity Gradients and Color Histograms, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR 98, Santa Barbara, California, pp:232-237, June 1998
4. M. La Cascia, S. Sclaroff, and V. Athitsos, Fast, Reliable Head Tracking under Varying Illumination: An Approach Based on Robust Registration of Texture-Mapped 3D Models, IEEE Trans. Pattern Analysis and Machine Intelligence, vol.22, no.4, April, 2000
5. S.Z. Li, Q.D. Fu, L. Gu, B. Scholkopf, Y.M. Cheng, H.J. Zhang, Kernel Machine Based Learning for Multi-View Face Detection and Pose Estimation. In Proceedings of 8th IEEE International Conference on Computer Vision, ICCV2001,. Vancouver, Canada. July 9-12, 2001.
6. Y.Li, S.Gong, and H. Liddell, Multi-view face detection using support vector machines and eigenspace modeling. In Proceeding of International Conference on Knowledge-based Intelligent Engineering Systems and Allied Technologies, Brighton, UK, pp. 241-245. September 2000.
7. S. Mika, G. Ratsch, J. Weston, B. Scholkopf, and K.-R. Miiller. Fisher discriminant analysis with kernels. In Y.-H. Hu, J. Larsen, E. Wilson, and S. Douglas, editors, Neural Networks for Signal Processing IX, pp 41-48. IEEE, 1999
8. B.Moghaddam and A.Pentland, Probabilistic Visual Learning for Object Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, no.7, pp.696-710, 1997.
9. S.Roweis, L.Saul, Nonlinear dimensionality reduction by locally linear embedding. Science, vol.290, no.5500, pp. 2323-2326, Dec. 22, 2000.
10. B. Scholkopf, J. Piatt, J. Shawe-Taylor, A.J. Smola, and R. C.Williamson, Estimating the support of a high-dimensional distribution. Neural Computation,vo\.l3, pp:1443-1471, 2001.
11. B. Scholkopf, A.J. Smola, and K.-R. Muller. Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation, vol.10 pp:1299-1319, 1998.
12. J. B. Tenenbaum, V. de Silva, and J. C. Langford, A Global Geometric Framework for Nonlinear Dimensionality Reduction, Science, vol.290 no.5500, pp:2319-2323, 22 December 2000
13. V Vapnik, The Nature of Statistic Learning Theory, Springer, N.Y. 1995
14. W.Zhao, R.Chellappa, and A. Krishnaswamy, Discriminant Analysis of Principal Components for Face Recognition, In Proceeding of International Conference on Automatic Face and Gesture Recognition, FG98, pp.336-341, 1998.