Exploring Avatar Facial Fidelity and Emotional Expressions on Observer Perception of the Uncanny Valley

  • Jacqueline Bailey
  • Karen Blackmore
  • Grant Robinson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10711)


Avatars or digital human characters are traditionally associated with digital games, where they enhance story-based interaction and player engagement. However, interest in accurate and cost-effective avatar development has increased in fields such as serious gaming and simulation training. The interest from these fields stems from training needs where avatars, used create human emotional experiences, could aid in appropriate education. In this context, the area of avatar emotional expression is not well understood. Applications often result in avatars that produce feelings of uncanniness in end-users, which may have a negative impact on training outcomes. This paper aims to firstly explore how avatar fidelity or realism could influence the emotional experience of interactions between end users and virtual humans. Secondly, we examine how avatar facial features displaying emotional expressions affect participants perceived valence of the avatar. These affects were assessed through a combination of experimental and survey methodologies. An existing ‘Godspeed’ survey was used to measure an end-users’ perception of an avatars ‘humanness’, ‘eeriness’, and ‘attractiveness,’ together with a three-part experiment that measured participants’ startle reflex responses to avatars with differing fidelity and emotional expressions. The results indicated that participant gender played a role in their perception of avatars. Also, the gender of the avatar appeared to have a significant impact on participant responses. Avatars displaying sad expressions emerged as less unpleasant and possibly less uncanny than smiling expressions. This research represents an entry point in a broad, cross-disciplinary area of research. While there are important findings and a significant amount of data generated, these elements pose questions for future work in this area.


Avatar Facial fidelity Emotional expression 



This research was completed with the assistance of the Australian Defence College (ADC) - Simulation Centre, Williamtown, NSW Australia and with the assistance of the University of Southern California Institute for Creative Technologies (ICT) Graphics Lab.


  1. 1.
    Mori, M., MacDorman, K.F., Kageki, N.: The uncanny valley [from the field]. IEEE Robot. Autom. Mag. 19(2), 98–100 (2012)CrossRefGoogle Scholar
  2. 2.
    Wrzesien, M., et al.: How the physical similarity of avatars can influence the learning of emotion regulation strategies in teenagers. Comput. Hum. Behav. 43, 101–111 (2015)CrossRefGoogle Scholar
  3. 3.
    Harrell, D.F., Harrell, S.V.: Imagination, computation, and self-expression: situated character and avatar mediated identity. Leonardo Electron. Alm. 17(2), 74–91 (2012)CrossRefGoogle Scholar
  4. 4.
    Huang, H., et al.: Leveraging motion capture and 3D scanning for high-fidelity facial performance acquisition. ACM Trans. Graph. (TOG) 30(4), 74 (2011)CrossRefGoogle Scholar
  5. 5.
    Weise, T., et al.: Realtime performance-based facial animation. ACM Trans. Graph. (TOG) 30(4), 77 (2011)CrossRefGoogle Scholar
  6. 6.
    Karras, T., et al.: Progressive growing of GANs for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196 (2017)
  7. 7.
    FaceShift. FaceShift AG (2015)Google Scholar
  8. 8.
    Yang, S., Bhanu, B.: Understanding discrete facial expressions in video using an emotion avatar image. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 42(4), 980–992 (2012)CrossRefGoogle Scholar
  9. 9.
    Wang, Y., Geigel, J.: Using facial emotional signals for communication between emotionally expressive avatars in virtual worlds. In: D’Mello, S., Graesser, A., Schuller, B., Martin, J.-C. (eds.) ACII 2011. LNCS, vol. 6975, pp. 297–304. Springer, Heidelberg (2011). Scholar
  10. 10.
    Dictionary, O.E.: Fordham University Library Database (2015)Google Scholar
  11. 11.
    Courgeon, M., Buisine, S., Martin, J.-C.: Impact of expressive wrinkles on perception of a virtual character’s facial expressions of emotions. In: Ruttkay, Z., Kipp, M., Nijholt, A., Vilhjálmsson, H.H. (eds.) IVA 2009. LNCS (LNAI), vol. 5773, pp. 201–214. Springer, Heidelberg (2009). Scholar
  12. 12.
    Alkawaz, M.H., Basori, A.H.: The effect of emotional colour on creating realistic expression of avatar. In: Proceedings of the 11th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry. ACM (2012)Google Scholar
  13. 13.
    Proulx, T., Heine, S.J., Vohs, K.D.: When is the unfamiliar the uncanny? Meaning affirmation after exposure to absurdist literature, humor, and art. Pers. Soc. Psychol. Bull. 36(6), 817–829 (2010)CrossRefGoogle Scholar
  14. 14.
    Freud, S.: The ‘Uncanny’, vol. 17, pp. 218–256. Hogarth Press, London (1919)Google Scholar
  15. 15.
    Jentsch, E.: Zur psychologie des unheimlichen (1906)Google Scholar
  16. 16.
    Lang, P.J., Bradley, M.M., Cuthbert, B.N.: International affective picture system (IAPS): technical manual and affective ratings. The Center for Research in Psychophysiology, University of Florida, Gainesville (1999)Google Scholar
  17. 17.
    Nesbitt, K., Blackmore, K., Hookham, G., Kay-Lambkin, F., Walla, P.: Using the startle eye-blink to measure affect in players. In: Loh, C.S., Sheng, Y., Ifenthaler, D. (eds.) Serious Games Analytics. AGL, pp. 401–434. Springer, Cham (2015). Scholar
  18. 18.
    Witvliet, C.V., Vrana, S.R.: Psychophysiological responses as indices of affective dimensions. Psychophysiology 32(5), 436–443 (1995)CrossRefGoogle Scholar
  19. 19.
    Ho, C.-C., MacDorman, K.F.: Revisiting the uncanny valley theory: developing and validating an alternative to the Godspeed indices. Comput. Hum. Behav. 26(6), 1508–1518 (2010)CrossRefGoogle Scholar
  20. 20.
    Lang, P.J., Bradley, M.M., Cuthbert, B.N.: International affective picture system (IAPS): affective ratings of pictures and instruction manual. Technical report A-8 (2008)Google Scholar
  21. 21.
    Bartholow, B.D., Bushman, B.J., Sestir, M.A.: Chronic violent video game exposure and desensitization to violence: behavioral and event-related brain potential data. J. Exp. Soc. Psychol. 42(4), 532–539 (2006)CrossRefGoogle Scholar
  22. 22.
    Blumenthal, T.D., et al.: Committee report: guidelines for human startle eyeblink electromyographic studies. Psychophysiology 42(1), 1–15 (2005)CrossRefGoogle Scholar
  23. 23.
    Bartneck, C., et al.: Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. Int. J. Soc. Robot. 1(1), 71–81 (2009)CrossRefGoogle Scholar
  24. 24.
    Creswell, J.W.: Qualitative Inquiry and Research Design: Choosing Among Five Traditions. Sage, Thousand Oaks (2007)Google Scholar
  25. 25.
    Hess, U., Sabourin, G., Kleck, R.E.: Postauricular and eyeblink startle responses to facial expressions. Psychophysiology 44(3), 431–435 (2007)CrossRefGoogle Scholar
  26. 26.
    Urdan, T.: Introduction to social science research: principles and terminology. In: Statistics in Plain English, pp. 1–12 (2010)Google Scholar
  27. 27.
    Sutton, K., Heathcote, A., Bore, M.: Measuring 3-D understanding on the web and in the laboratory. Behav. Res. Methods 39(4), 926–939 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of NewcastleNewcastleAustralia
  2. 2.Australian Defence College Simulation DirectorateWilliamtownAustralia

Personalised recommendations