Robust head pose estimation using supervised manifold learning

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6316 LNCS, Issue PART 6, 2010, Pages 518-531

  BenAbdelkader, Chiraz ^a  

^a NEW YORK INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

dimensionality reduction; head pose estimation; manifold learning; nonlinear regression; supervised learning

Indexed keywords

COMPUTER VISION; EMBEDDINGS; NONLINEAR ANALYSIS; REGRESSION ANALYSIS; SUPERVISED LEARNING;

DIMENSIONALITY REDUCTION; HEAD POSE ESTIMATION; LOCALITY PRESERVING PROJECTIONS; LOW-DIMENSIONAL MANIFOLDS; MANIFOLD LEARNING; NEIGHBORHOOD PRESERVING EMBEDDING; NON-LINEAR REGRESSION; SUPERVISED MANIFOLD LEARNING;

LEARNING SYSTEMS;

EID: 78149339402     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15567-3_38     Document Type: Conference Paper

References (33)

1. Tian, Y., Brown, L., Connell, J.: Absolute head pose estimation from overhead wide-angle cameras. In: IEEE Workshop on Analysis and Modeling of Faces and Gestures (2003)
2. Wu, J., Trivedi, M.M.: A two-stage pose estimation framework and evaluation. Pattern Recognition 41, 1138-1158 (2008) (Pubitemid 47632672)
3. Murphy-Chutorian, E., Trivedi, M.: Head pose estimation in computer vision: A survey. 31, 607-626 (2009)
4. Orozco, J., Gong, S.: Head pose classification in crowded scenes. In: British Machine Vision Conference (2009)
5. Brown, L., Tian, Y.: Comparative study of coarse head pose estimation. In: IEEE Workshop on Motion and Video Processing (2002)
6. Saul, L.K., Roweis, S.T.: Think globally, fit locally: unsupervised learning of low dimensional manifolds. Journal of Machine Learning Research 4, 119-155 (2003)
7. Burges, C.J.C.: Geometric Methods for Feature Extraction and Dimensional Reduction. In: Book, Kluwer Academic Publishers, Dordrecht (2005)
8. van der Maaten, L., Postma, E., van den Herik,H.: Dimensionality reduction: A comparative review. Technical Report TiCC-TR 2009-005, Tilburg University (2009)
9. Raytchev, B., Yoda, I., Sakaue, K.: Head pose estimation by nonlinear manifold learning. In: ICPR, pp. 462-466 (2004)
10. Geng, X., Chuan Zhan, D., Hua Zhou, Z.: Supervised nonlinear dimensionality reduction for visualization and classification. IEEE Transactions on Systems, Man, and Cybernetics: Part B 35, 1098-1107 (2005)
11. Fu, Y., Huang, T.S.: Graph embedded analysis for head pose estimation. In: FGR, pp. 3-8 (2006)
12. Sugiyama, M.: Local fisher discriminant analysis for supervised dimensionality reduction. In: International Conference on Machine learning, pp. 905-912 (2006)
13. Sugiyama, M.: Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. Journal of Machine Learning Research 8, 1027-1061 (2007)
14. 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)
15. He, X., Ji, M., Bao, H.: Graph embedding with constraints. In: IJCAI, pp. 1065-1070 (2009)
16. Yang, M.H.: Extended isomap for pattern classification. In: ICPR (2002)
17. He, X., Yan, S., Hu, Y., Niyogi, P., Zhang, H.J.: Face recognition using laplacian-faces. PAMI 27, 328-340 (2005)
18. Chen, H.T., Chang, H.W., Liu, T.L.: Local discriminant embedding and its variants. In: CVPR, pp. 846-853 (2005)
19. Zhao, Q., Zhang, D., Lu, H.: Supervised LLE in ICA space for facial expression recognition. In: International Conference on Neural Networks and Brain, pp. 1970-1975 (2005)
20. Teoh, A.B.J., Pang, Y.H.: Analysis on supervised neighborhood preserving embedding. IEICE Electronics Express 6, 1631-1637 (2009)
21. Nilsson, J., Sha, F., Jordan, M.I.: Regression on manifolds using kernel dimension reduction. In: International Conference on Machine Learning, pp. 697-704 (2007)
22. Balasubramanian, V.N., Ye, J., Panchanathan, S.: Biased manifold embedding: a framework for person-independent head pose estimation. In: CVPR (2007)
23. Balasubramanian, V.N., Krishna, S., Panchanathan, S.: Person-independent head pose estimation using biased manifold embedding. Eurasip Journal on Advances in Signal Processing 2008, 1-15 (2008)
24. He, X., Cai, D., Yan, S., Zhang, H.J.: Neighborhood preserving embedding. In: ICCV, pp. 1208-1213 (2005)
25. He, X., Niyogi, P.: Locality preserving projections. Advances in Neural Information Processing Systems 16, 100-200 (2004)
26. Little, G., Krishna, S., Black, J., Panchanthan, S.: A methodology for evaluating robustness of face recognition algorithms with respect to variations in pose angle and illumination angle. In: International Conference on Acoustics, Speech, and Signal Processing (2005)
27. Yan, S., Xu, D., Zhang, B., Zhang, H.J., Yang, Q., Lin, S.: Graph embedding and extensions: A general framework for dimensionality reduction. PAMI 29, 40-51 (2007) (Pubitemid 46415944)
28. Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Journal of Neural Computation 15, 1373-1396 (2003) (Pubitemid 37049796)
29. Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323-2326 (2000)
30. Zeng, X., Luo, S.: A supervised subspace learning algorithm: Supervised neighborhood preserving embedding. In: International Conference on Advanced Data Mining and Applications (2007)
31. de Ridder, D., Kouropteva, O., Okun, O., Pietikùinen, M., Duin, R.P.: Supervised locally linear embedding. In: International Conference on Artificial Neural Networks and Neural Information Processing (2003)
32. Li, C.G., Guo, J.: Supervised isomap with explicit mapping. In: Innovative Computing, Innovation, and Control (2006)
33. Tenenbaum, J.B., Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319-2323 (2000) (Pubitemid 32041577)