Dynamic probabilistic CCA for analysis of affective behavior and fusion of continuous annotations

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 36, Issue 7, 2014, Pages 1299-1311

  Nicolaou, Mihalis A ^a   Pavlovic, Vladimir ^b   Pantic, Maja ^a,c  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b RUTGERS UNIVERSITY   (United States)

^c UNIVERSITY OF TWENTE   (Netherlands)

Author keywords

affect analysis; component analysis; dimensional emotion; Fusion of continuous annotations; temporal alignment

Indexed keywords

ARTIFICIAL INTELLIGENCE;

AFFECT ANALYSIS; AFFECTIVE BEHAVIORS; CANONICAL CORRELATION ANALYSIS; COMPONENT ANALYSIS; DIMENSIONAL EMOTION; EXPERT ANNOTATIONS; STATE-OF-THE-ART METHODS; TEMPORAL ALIGNMENT;

ALIGNMENT;

EID: 84903205280     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2014.16     Document Type: Article

References (30)

1. H. Gunes, B. Schuller, M. Pantic, and R. Cowie, "Emotion representation, analysis and synthesis in continuous space: A survey," in Proc. IEEE Int. Conf. EmoSPACE WS, Santa Barbara, CA, USA, 2011, pp. 827-834
2. R. Cowie and G. McKeown. (2010). Statistical Analysis of Data From Initial Labelled Database and Recommendations for an Economical Coding Scheme
3. [Online]. Available: http://www.semaine-project.eu/
4. M. Wöllmer et al.,"Abandoning emotion classes," in Proc. INTERSPEECH, 2008, pp. 597-600
5. M. A. Nicolaou, H. Gunes, and M. Pantic, "Continuous prediction of spontaneous affect from multiple cues and modalities in valence-arousal space," IEEE Trans. Affect. Comput., vol. 2, no. 2, pp. 92-105, Apr.-Jun. 2011
6. M. Grimm, K. Kroschel, E.Mower, and S. Narayanan, "Primitivesbased evaluation and estimation of emotions in speech," Speech Commun., vol. 49, no. 10-11, pp. 787-800, Oct. 2007 (Pubitemid 47268568)
7. K. Audhkhasi and S. S. Narayanan, "A globally-variant locallyconstant model for fusion of labels from multiple diverse experts without using reference labels," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 4, pp. 769-783, Apr. 2013
8. V. C. Raykar et al., "Learning from crowds," J. Mach. Learn. Res., vol. 11, pp. 1297-1322, Apr. 2010
9. F. Zhou and F. De la Torre, "Canonical time warping for alignment of human behavior," in Proc. Adv. NIPS, 2009, pp. 2286-2294
10. A. Klami and S. Kaski, "Probabilistic approach to detecting dependencies between data sets," Neurocomput., vol. 72, no. 1-3, pp. 39-46, Dec. 2008
11. F. R. Bach and M. I. Jordan, "A probabilistic interpretation of canonical correlation analysis," Univ. California, Berkeley, CA, USA, Tech. Rep. 688, 2006
12. L. R. Tucker, "An inter-battery method of factor analysis," Psychometrika, vol. 23, no. 2, pp. 111-136, 1958
13. M. W. Browne, "The maximum-likelihood solution in interbattery factor analysis," Br. J. Math. Stat. Psychol., vol. 32, no. 1, pp. 75-86, 1979
14. Z. Zeng, M. Pantic, G. I. Roisman, and T. S. Huang, "A survey of affect recognition methods: Audio, visual, and spontaneous expressions," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 1, pp. 39-58, Jan. 2009
15. M. Kim and V. Pavlovic, "Discriminative learning for dynamic state prediction," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 10, pp. 1847-1861, Oct. 2009
16. Z. Ghahramani and S. T. Roweis, "Learning nonlinear dynamical systems using an EM algorithm," in Advances NIPS. Cambridge, MA, USA: MIT Press, 1999, pp. 599-605
17. S. Roweis and Z. Ghahramani, "A unifying review of linear Gaussian models," Neural Comput., vol. 11, no. 2, pp. 305-345, 1999
18. Z. Ghahramani, M. I. Jordan, and P. Smyth, "Factorial hidden Markov models," in Machine Learning. Cambridge, MA, USA: MIT Press, 1997
19. C. M. Bishop, Pattern Recognition and Machine Learning. Secaucus, NJ, USA: Springer-Verlag, Inc., 2006
20. R. Van der Merwe and E. Wan, "The square-root unscented Kalman filter for state and parameter-estimation," in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Process., vol. 6. Salt Lake City, UT, USA, 2001, pp. 3461-3464 (Pubitemid 32838365)
21. M. A. Hasan, "On multi-set canonical correlation analysis," in Proc. Int. Joint Conf. Neural Netw., Piscataway, NJ, USA: IEEE Press, 2009, pp. 2640-2645
22. G. McKeown, M. F. Valstar, R. Cowie, and M. Pantic, "The SEMAINE corpus of emotionally coloured character interactions," in Proc. IEEE ICME, Suntec City, Singapore, 2010, pp. 1079-1084
23. L. Rabiner and B. H. Juang, Fundamentals of Speech Recognition. Englewood Cliffs, NJ, USA: Prentice Hall, Apr. 1993
24. F. Zhou and F. De la Torre Frade, "Generalized time warping for multi-modal alignment of human motion," in Proc. IEEE CVPR, Providence, RI, USA, Jun. 2012, pp. 1282-1289
25. M. A. Larkin et al., "Clustal W and clustal X version 2.0," Bioinformatics, vol. 23, no. 21, pp. 2947-2948, 2007 (Pubitemid 350162903)
26. S. Yu, K. Yu, V. Tresp, H.-P. Kriegel, and M. Wu, "Supervised probabilistic principal component analysis," in Proc. 12th ACM SIGKDD Int. Conf. KDD, New York, NY, USA, 2006, pp. 464-473 (Pubitemid 44535543)
27. R. J. Aumann, "Agreeing to disagree," Ann. Statist., vol. 4, no. 6, pp. 1236-1239, 1976
28. J. Shi and J. Malik, "Normalized cuts and image segmentation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 8, pp. 888-905, Aug. 2000
29. I. Patras and M. Pantic, "Particle filtering with factorized likelihoods for tracking facial features," in Proc. IEEE Int. Conf. Autom. FGR, Washington, DC, USA, 2004, pp. 97-102
30. M. E. Tipping, "Sparse Bayesian learning and the relevance vector machine," J. Mach. Learn. Res., vol. 1, pp. 211-244, Jun. 2001. (Pubitemid 33687203)