Non-frontal view facial expression recognition based on ergodic hidden Markov model supervectors

2010 IEEE International Conference on Multimedia and Expo, ICME 2010

Volumn , Issue , 2010, Pages 1202-1207

  Tang, Hao ^a   Hasegawa Johnson, Mark ^a   Huang, Thomas ^a  

^a University of Illinois at Urbana Champaign   (United States)

Author keywords

Facial expression recognition; Hidden Markov model; Supervector representation

Indexed keywords

AUTOMATIC FACIAL EXPRESSION RECOGNITION; CLASSIFICATION ALGORITHM; ERGODICS; FACIAL EXPRESSION RECOGNITION; FACIAL EXPRESSIONS; FACIAL IMAGES; FEATURE VECTORS; K-NEAREST NEIGHBORS; KULLBACK LEIBLER DIVERGENCE; LINEAR DISCRIMINANTS; MAXIMUM A POSTERIORI; MULTI-VIEWS; NON-FRONTAL VIEWS; PROBLEM-BASED; RESEARCH COMMUNITIES; RESEARCH TOPICS; SCALE INVARIANT FEATURE TRANSFORMS; SUPERVECTOR; TEST SETS; TILT ANGLE; TRAINING SETS; UNIVERSAL BACKGROUND MODEL; UNIVERSAL FACIAL EXPRESSIONS; UPPER BOUND APPROXIMATION;

APPROXIMATION ALGORITHMS; GESTURE RECOGNITION; HIDDEN MARKOV MODELS; SPEECH RECOGNITION;

FACE RECOGNITION;

EID: 78349248247     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICME.2010.5582576     Document Type: Conference Paper

References (22)

1. B. Fasel, and J. Luettin, "Automatic Facial Expression Analysis: A Survey," Pattern Recognition, 36:259-275, 1999.
2. M. Pantic, and L.J.M. Rothkrantz, "Automatic Analysis of Facial Expressions: the State of the Art," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 12, pp. 1424-1445, 2000.
3. Y.L. Tian, T. Kanade, and J.F. Cohn, "Facial expression analysis," In: S.Z. Li, A.K. Jain (eds.), Handbook of Facial Recognition, Springer, New York, USA, pp. 247-276, 2005.
4. L. Yin, X. Wei, Y. Sun, J. Wang, and M.J. Rosato, "A 3D facial expression database for facial behavior research," Proceedings of 7th International Conference on Automatic Face and Gesture Recognition, pp. 211-216, 2006.
5. Y. Hu, Z. Zeng, L. Yin, X.Wei, J. Tu, and T.S. Huang, "A study of non-frontal-view facial expressions recognition," Proceedings of International Conference on Pattern Recognition, pp. 1-4, 2008.
6. W. Zheng, H. Tang, Z. Lin, and T.S. Huang, "A novel approach to expression recognition from non-frontal face images," Proceedings of IEEE International Conference on Computer Vision, pp. 1901-1908, 2009.
7. D.G. Lowe, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.
8. L.R. Rabiner, "A tutorial on Hidden Markov Models and selected applications in speech recognition," Proceedings of the IEEE, 77 (2): 257-286, 1989.
9. J.-L. Gauvain, and C.-H. Lee, "Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains," IEEE Trans. Speech and Audio Processing, (2):291-298, 2004.
10. S. Kullback, and R. Leibler, "On Information and Sufficiency," Ann. Math. Statist., vol. 22, no. 1, pp. 79-86, 1951.
11. J.R. Norris, Markov Chains, Cambridge series in Statistical and Probabilistic Mathematics, 1999.
12. OpenGL: The Industry's Foundation for High Performance Graphics, http://www.opengl.org/.
13. Stephen Levinson, Mathematical Models for Speech Technology, John Wiley & Sons, Ltd, 2005.
14. L.E. Baum, T. Petrie, G. Soules, and N. Weiss, "A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains," Ann. Math. Statist., vol. 41, no. 1, pp. 164-171, 1970.
15. D. A. Reynolds, T. F. Quatieri and R. B. Dunn, "Speaker Verification using Adapted Gaussian Mixture Models," Digital Signal Processing, vol. 10, no. 1-3, 2000.
16. T.M. Cover, and J.A. Thomas, Elements of Information Theory, 1st ed., John Wiley & Sons, Inc., New York, NY, 1991.
17. Z. Rached, F. Alalaji, and L.L. Campbell, "The Kullback-Leibler divergence rate between Markov sources," IEEE Trans. Info. Thy., vol. 50, no. 5, pp. 917-921, 2004.
18. M. Do, and M. Vetterli, "Fast approximation of Kullback-Leibler distance for dependence trees and hidden Markov models," IEEE Signal Processing Letters, vol. 10, no. 4, pp. 115118, April 2003.
19. D.A. Reynolds, "Speaker identification and verification using Gaussian mixture speaker models," Speech Communication, vol. 17, iss. 1-2, pp. 91-108, 1005.
20. R.O. Duda, P.E. Hart, and D.G. Stork. Pattern Classification. JohnWiley & Sons, Inc., 2nd edition, 2001.
21. W.M. Campbell, "A Covariance Kernel for SVM Language Recognition," ICASSP 2008, Las Vegas, NV, 2008.
22. C.M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.