Multi-output Laplacian dynamic ordinal regression for facial expression recognition and intensity estimation

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2012, Pages 2634-2641

  Rudovic, Ognjen ^a   Pavlovic, Vladimir ^b   Pantic, Maja ^c  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b RUTGERS UNIVERSITY   (United States)

^c UNIVERSITY OF TWENTE   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

BINARY CLASSIFIERS; DYNAMIC RECOGNITION; FACIAL EXPRESSION RECOGNITION; FACIAL EXPRESSIONS; INTENSITY ESTIMATION; LAPLACIANS; MULTI-CLASS; MULTI-OUTPUT; ORDINAL REGRESSION; RANDOM FIELDS; TEMPORAL EVOLUTION;

GESTURE RECOGNITION; TOPOLOGY;

COMPUTER VISION;

EID: 84866674821     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2012.6247983     Document Type: Conference Paper

References (26)

1. Z. Ambadar, J. Schooler, and J. Cohn. Deciphering the enigmatic face: The importance of facial dynamics to interpreting subtle facial expressions. Psychological Science, 16(5):403-410, 2005. 1
2. N. Ambady and R. Rosenthal. Thin slices of expressive behavior as predictors of interpersonal consequences: a meta-analysis. Psychological Bulletin, 111:256-274, 1992. 1
3. W. Chu and Z. Ghahramani. Gaussian processes for ordinal regression. JMLR, 6:1019-1041, 2005. 2
4. W. Chu and S. S. Keerthi. New approaches to support vector ordinal regression. pages 145-152, 2005. ICML. 2
5. J. Delannoy and J. McDonald. Automatic estimation of the dynamics of facial expression using a three-level model of intensity. FG, pages 1-6, 2008. 1
6. F. Dornaika and J. Orozco. Real time 3d face and facial feature tracking. J. Real-Time Image Processing, pages 35-44, 2007. 5, 7
7. H. Gunes and M. Piccardi. Automatic temporal segment detection and affect recognition from face and body display. IEEE Trans. on Systems, Man, and Cybernetics, 39(1):64-84, 2009. 1
8. X. He and P. Niyogi. Locality Preserving Projections. NIPS, 2004. 3
9. M. Kim and V. Pavlovic. Hidden conditional ordinal random fields for sequence classification. Machine Learning and Knowledge Discovery in Databases, 6322:51-65, 2010. 1, 2, 3, 4, 5, 6, 7
10. M. Kim and V. Pavlovic. Structured output ordinal regression for dynamic facial emotion intensity prediction. ECCV, pages 649-662, 2010. 1, 2
11. S. Koelstra, M. Pantic, and I. Patras. A dynamic texture based approach to recognition of facial actions and their temporal models. IEEE PAMI, 32:1940-1954, 2010. 1
12. J. Lafferty, A. McCallum, and F. Pereira. Conditional Random Fields: Probabilistic models for segmenting and labeling sequence data. pages 282-289, 2001. ICML. 2
13. J. Lien, T. Kanade, J. Cohn, and C. Li. Detection, tracking, and classification of action units in facial expression. J. of Rob. and Aut.Systems, 31(3):131-146. 5
14. M. Mahoor, S. Cadavid, D. Messinger, and J. Cohn. A framework for automated measurement of the intensity of non-posed facial action units. CVPRW, pages 74-80, 2009. 1
15. I. Patras and M. Pantic. Particle Filtering with Factorized Likelihoods for Tracking Facial Features. FG, pages 97-102, 2004. 5, 7
16. A. Quattoni, M. Collins, and T. Darrell. Conditional random fields for object recognition. NIPS, pages 1097-1104, 2004. 3
17. A. Samal and P. A. Iyengar. Automatic recognition and analysis of human faces and facial expressions: a survey. Pattern Recognition, 25(1):65-77, 1992. 1
18. A. Savrana, B. Sankur, and M. Bilgeb. Regression-based intensity estimation of facial action units. Image and Vision Computing, 2012. 1
19. C. Shan, S. Gong, and P.W. Mcowan. Appearance manifold of facial expression. Lecture Notes in Comp. Science, 3766:221-230, 2005. 3
20. C. Shan, S. Gong, and P. W. Mcowan. Dynamic facial expression recognition using a bayesian temporal manifold model. BMVC, pages 297-306, 2006. 1
21. A. Shashua and A. Levin. Ranking with large margin principle: Two approaches. pages 973-944, 2002. NIPS. 2
22. M. F. Valstar and M. Pantic. Fully automatic recognition of the temporal phases of facial actions. IEEE Trans. on Systems, Man, and Cybernetics, 42(1):28-43, 2012. 1
23. P. Yang, Q. Liu, and D. N. Metaxas. Rankboost with l1 regularization for facial expression recognition and intensity estimation. ICCV, pages 1018-1025, 2009. 1
24. L. Yin, X. Chen, Y. Sun, T. Worm, and M. Reale. A high-resolution 3d dynamic facial expression database. FG, pages 679-684, 2008. 5
25. Z. Zeng, M. Pantic, G. Roisman, and T. Huang. A survey of affect recognition methods: Audio, visual, and spontaneous expressions. IEEE PAMI, 31:39-58, 2009. 1
26. G. Zhong, W. Li, D. Yeung, X. Hou, and C. Liu. Gaussian process latent random field. 2010. AAAI. 4