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A Note on Modelling a Somatic Motor Space for Affective Facial Expressions

  • Alessandro D’Amelio
  • Vittorio Cuculo
  • Giuliano Grossi
  • Raffaella LanzarottiEmail author
  • Jianyi Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10590)

Abstract

We discuss modelling issues related to the design of a somatic facial motor space. The variants proposed are conceived to be part of a larger system for dealing with simulation-based face emotion analysis along dual interactions.

Keywords

Emotion Human-agent interaction Simulation Kalman filter Probabilistic generative models 

Notes

Acknowledgments

This research was carried out as part of the project “Interpreting emotions: a computational tool integrating facial expressions and biosignals based shape analysis and bayesian networks”, supported by the Italian Government, managed by MIUR, financed by the Future in Research Fund.

References

  1. 1.
    Ahlberg, J.: CANDIDE-3 an updated parameterized face. Technical report. LiTH-ISY-R-2326, Linköping University, Department of Electrical Engineering, Linköping, Sweden (2010)Google Scholar
  2. 2.
    Baltrušaitis, T., Robinson, P., Morency, L.P.: Constrained local neural fields for robust facial landmark detection in the wild. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 354–361 (2013)Google Scholar
  3. 3.
    Cuculo, V., Lanzarotti, R., Boccignone, G.: Using sparse coding for landmark localization in facial expressions. In: 5th European Workshop on Visual Information Processing (EUVIP), pp. 1–6, December 2014Google Scholar
  4. 4.
    Damianou, A.C., Titsias, M.K., Lawrence, N.D.: Variational inference for latent variables and uncertain inputs in Gaussian processes. J. Mach. Learn. Res. (JMLR) 17(1), 1425–1486 (2016)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Ekman, P., Rosenberg, E.L.: What the Face Reveals: Basic and Applied Studies of Spontaneous Expression Using the Facial Action Coding System (FACS). Oxford University Press, New York (1997)Google Scholar
  6. 6.
    Fan, P., Gonzalez, I., Enescu, V., Sahli, H., Jiang, D.: Kalman filter-based facial emotional expression recognition. In: D’Mello, S., Graesser, A., Schuller, B., Martin, J.-C. (eds.) ACII 2011. LNCS, vol. 6974, pp. 497–506. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-24600-5_53 CrossRefGoogle Scholar
  7. 7.
    García, H.F., Álvarez, M.A., Orozco, Á.: Gaussian process dynamical models for emotion recognition. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., McMahan, R., Jerald, J., Zhang, H., Drucker, S.M., Kambhamettu, C., El Choubassi, M., Deng, Z., Carlson, M. (eds.) ISVC 2014. LNCS, vol. 8888, pp. 799–808. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-14364-4_77 Google Scholar
  8. 8.
    von Helmholtz, H.: Über integrale der hydrodynamischen gleichungen welche den wirbelbewegungen entsprechen. Crelles J. 55, 25–55 (1858)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Lopes, M., Santos-Victor, J.: Visual learning by imitation with motor representations. IEEE Trans. Sys. Man Cybern. Part B Cybern. 35(3), 438–449 (2005)CrossRefGoogle Scholar
  10. 10.
    Orozco, J., Rudovic, O., Gonzlez, J., Pantic, M.: Hierarchical on-line appearancebased tracking for 3D head pose, eyebrows, lips, eyelids and irises. Image Vis. Comput. 31(4), 322–340 (2013)CrossRefGoogle Scholar
  11. 11.
    Pickering, M.J., Clark, A.: Getting ahead: forward models and their place in cognitive architecture. Trends Cogn. Sci. 18(9), 451–456 (2014)CrossRefGoogle Scholar
  12. 12.
    Rao, R.P., Ballard, D.H.: Dynamic model of visual recognition predicts neural response properties in the visual cortex. Neural Comput. 9(4), 721–763 (1997)CrossRefGoogle Scholar
  13. 13.
    Russell, J.A.: Core aect and the psychological construction of emotion. Psychol. Rev. 110(1), 145 (2003)CrossRefGoogle Scholar
  14. 14.
    Sariyanidi, E., Gunes, H., Cavallaro, A.: Automatic analysis of facial affect: a survey of registration, representation, and recognition. IEEE Trans. Patt. Anal. Mach. Intell. 37(6), 1113–1133 (2015)CrossRefGoogle Scholar
  15. 15.
    Vitale, J., Williams, M.A., Johnston, B., Boccignone, G.: Affective facial expression processing via simulation: a probabilistic model. Biologically Inspired Cogn. Architectures J. 10, 30–41 (2014)CrossRefGoogle Scholar
  16. 16.
    Wood, A., Rychlowska, M., Korb, S., Niedenthal, P.: Fashioning the face: sensorimotor simulation contributes to facial expression recognition. Trends Cogn. Sci. 20(3), 227–240 (2016)CrossRefGoogle Scholar
  17. 17.
    Zhu, X., Ramanan, D.: Face detection, pose estimation, and landmark localization in the wild. In: Proceedings of IEEE CVPR, pp. 2879–2886 (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Alessandro D’Amelio
    • 1
  • Vittorio Cuculo
    • 1
    • 2
  • Giuliano Grossi
    • 1
  • Raffaella Lanzarotti
    • 1
    Email author
  • Jianyi Lin
    • 3
  1. 1.PHuSe Lab - Dipartimento di InformaticaUniversità degli Studi di MilanoMilanoItaly
  2. 2.Dipartimento di MatematicaUniversità degli Studi di MilanoMilanoItaly
  3. 3.Department of MathematicsKhalifa University of Science and TechnologyAbu DhabiUnited Arab Emirates

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