Dimensional affect recognition using Continuous Conditional Random Fields

2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013

Volumn , Issue , 2013, Pages

  Baltrusaitis, Tadas ^a   Banda, Ntombikayise ^a   Robinson, Peter ^a  

^a UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

AFFECT RECOGNITION; AUTOMATIC RECOGNITION OF EMOTIONS; CONDITIONAL RANDOM FIELD; DIMENSIONAL SPACES; FACIAL EXPRESSIONS; GEOMETRIC SHAPE; NON-ORTHOGONALITY; NON-VERBAL SIGNALS;

RANDOM PROCESSES;

IMAGE SEGMENTATION;

EID: 84881539534     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FG.2013.6553785     Document Type: Conference Paper

References (33)

1. B. Amberg, R. Knothe, and T. Vetter. Expression invariant 3D face recognition with a Morphable Model, pages 1-6. Ieee, Sep 2008.
2. T. Baltrusaitis, P. Robinson, and L.-P. Morency. 3D Constrained Local Model for Rigid and Non-Rigid Facial Tracking. In CVPR, 2012.
3. D. Cristinacce and T. Cootes. Feature detection and tracking with constrained local models. In BMVC, 2006.
4. P. Ekman. An argument for basic emotions. Cognition & Emotion, 6(3): 169-200, 1992.
5. R. Fan, K. Chang, C. Hsieh, X. Wang, and C. Lin. Liblinear: A library for large linear classification. The Journal of Machine Learning Research, 9: 1871-1874, 2008.
6. J. R. Fontaine, K. R. Scherer, E. B. Roesch, and P. Ellsworth. The world of emotion is not two-dimensional. Psychological Science, 18: 1050-1057, 2007.
7. H. Gunes, M. A. Nicolaou, and M. Pantic. Continuous Analysis of Affect from Voice and Face, pages 255-291. Springer London,. 2011
8. H. Gunes and M. Pantic. Automatic, dimensional and continuous emotion recognition. Int'l Journal of Synthetic Emotion, 1(1): 68-99, 2010.
9. L. Jeni, A. Lorincz, T. Nagy, Z. Palotai, J. Sebok, Z. Szabo, and D. Takacs. 3D shape estimation in video sequences provides high precision evaluation of facial expressions. Image and Vision Computing, 2012.
10. J. Lafferty, A. McCallum, and F. Pereira. Conditional random fields: probabilistic models for segmenting and labelling sequence data. In ICML, 2001.
11. M. Nicolaou, H. Gunes, and M. Pantic. Audio-visual classification and fusion of spontaneous affective data in likelihood space. In ICPR, 2010.
12. M. Nicolaou, H. Gunes, and M. Pantic. Output-Associative RVM Regression for Dimensional and Continuous Emotion Prediction. In IEEE FG'11, 2011.
13. T. Ojala, M. Pietikainen, and D. Harwood. A comparative study of texture measures with classification based on featured distributions. Pattern Recognition, 29(1): 51-59, 1996.
14. D. Ozkan, S. Scherer, and L.-P. Morency. Step-wise Emotion Recognition Using Concatenated-HMM. In 2nd International Audio/Visual Emotion Challenge and Workshop, 2012.
15. P. Paysan, R. Knothe, B. Amberg, S. Romdhani, and T. Vetter. A 3D Face Model for Pose and Illumination Invariant Face Recognition. In AVSS, 2009.
16. C. Pereira. Dimensions of emotional meaning in speech. In ISCA Tutorial and Research Workshop (ITRW) on Speech and Emotion, 2000.
17. R. W. Picard and J. Klein. Computers that recognise and respond to user emotion: Theoretical and practical implications. 2001.
18. T. Qin, T.-y. Liu, X.-d. Zhang, D.-s. Wang, and H. Li. Global ranking using continuous conditional random fields. In NIPS, 2008.
19. V. Radosavljevic, S. Vucetic, and Z. Obradovic. Continuous conditional random fields for regression in remote sensing. In European Conference on Artificial Intelligence, pages 809-814, 2010.
20. G. A. Ramirez, T. Baltrusaitis, and L.-P. Morency. Modeling latent discriminative dynamic of multi-dimensional affective signals. In ACII, Berlin, Heidelberg, 2011. Springer-Verlag.
21. P. Robinson and R. el Kaliouby. Computation of emotions in man and machines. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1535): 3441-3447, 2009.
22. J. Saragih, S. Lucey, and J. Cohn. Deformable Model Fitting by Regularized Landmark Mean-Shift. Int. J. Comput. Vision, 91(2): 200-215, Jan. 2011.
23. K. Scherer. Vocal correlates of emotional arousal and affective disturbance. Handbook of social psychophysiology, pages 165-197, 1989.
24. K. Scherer, T. Johnstone, and G. Klasmeyer. Vocal expression of emotion. Handbook of affective sciences, pages 433-456, 2003.
25. M. Schroder. Dimensional emotion representation as a basis for speech synthesis with non-extreme emotions. Affective dialogue systems, pages 209-220, 2004.
26. B. Schuller, M. Valstar, R. Cowie, and M. Pantic. Avec 2012-The continuous audio/visual emotion challenge. In Proceedings 2nd International Audio/Visual Emotion Challenge and Workshop, 2012.
27. B. Schuller, M. F. Valstar, R. Cowie, and M. Pantic. The first audio/visual emotion challenge and workshop-an introduction. In ACII, page 322, 2011.
28. C. Shan, S. Gong, and P. McOwan. Facial expression recognition based on local binary patterns: A comprehensive study. Image and Vision Computing, 27(6): 803-816, 2009.
29. C. Sutton and A. Mccallum. Introduction to Conditional Random Fields for Relational Learning. MIT Press, 2006.
30. M. Wollmer, F. Eyben, S. Reiter, B. Schuller, C. Cox, E. Douglas-Cowie, and R. Cowie. Abandoning emotion classes-towards continuous emotion recognition with modelling of long-range dependencies. In INTERSPEECH. ISCA, 2008.
31. L. Yin, X. Chen, Y. Sun, T. Worm, and M. Reale. A high-resolution 3D dynamic facial expression database. In FG, 2008.
32. Z. Zeng, M. Pantic, G. I. Roisman, and T. S. Huang. A survey of affect recognition methods: Audio, visual, and spontaneous expressions. TPAMI, 31(1), 2009.
33. G. Zhao and M. Pietikainen. Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE TPAMI, 29(6): 915-928, 2007.