Advertisement

Affective Gait Recognition and Baseline Evaluation from Real World Samples

  • Vili KellokumpuEmail author
  • Markus Särkiniemi
  • Guoying Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10116)

Abstract

Over the years a lot of research efforts have been put into recognizing human emotions from facial expressions. However, in many scenarios access to suitable face data is difficult, and therefore there is a need for methodology that can be used when people are observed from a distance. A potential modality for this is human gait. Early attempts to recognize human emotion from gait have been limited to acted data. Furthermore, in these approaches the data has been captured in controlled settings. This paper presents the first experiments for automated affective gait recognition using non acted real world samples. A database of 96 subjects affected by positive or negative feedback is collected and two baseline methods are used to recognize the affective state of a person. The baseline results are promising and encourage further study in this domain.

Notes

Acknowledgement

This work was sponsored by the Academy of Finland, Infotech Oulu and Nokia Visiting Professor grant.

References

  1. 1.
    Atkinson, A.P., Tunstall, M.L., Dittrich, W.H.: Evidence for distinct contributions of form and motion information to the recognition of emotions from body gestures. Cognition 104, 59–72 (2007)CrossRefGoogle Scholar
  2. 2.
    Boykov, Y., Kolmogorov, V.: An experimental comparison of Min-Cut/Max-Flow Algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1124–1137 (2004)CrossRefzbMATHGoogle Scholar
  3. 3.
    Crane, E., Gross, M.: Motion capture and emotion: affect detection in whole body movement. Affect. Comput. Intell. Interact. 4738, 95–101 (2007)CrossRefGoogle Scholar
  4. 4.
    Kellokumpu, V., Zhao, G., Li, S., Pietikäinen, M.: Dynamic texture based gait recognition. In: International Conference on Biometrics, ICB 2009 (2009)Google Scholar
  5. 5.
    Liu, Z., Sarkar, S.: Simplest representation yet for gait recognition: averaged silhouette In: Proceedings of the 17th International Conference on Pattern Recognition, vol. 4, pp. 211–214 (2004)Google Scholar
  6. 6.
    Montepare, J.M., Goldstein, S.B., Clausen, A.: The identification of emotions from gait information. J. Nonverbal Behav. 11(1), 33–42 (1987)CrossRefGoogle Scholar
  7. 7.
    Pollick, F.E., Paterson, H.M., Bruderlin, A., Sanford, A.J.: Perceiving affect from arm movement. Cognition 82, B51–B61 (2001)CrossRefGoogle Scholar
  8. 8.
    Roether, C.L., Omlor, L., Christensen, A., Giese, M.A.: Critical features for the perception of emotion from gait. J. Vis. 9, 1–32 (2009)CrossRefGoogle Scholar
  9. 9.
    Sarkar, S., Phillips, P.J., Liu, Z., Robledo, I., Grother, P., Bowyer, K.W.: The human ID gait challenge problem: data sets, performance, and analysis. IEEE Trans. Pattern Anal. Mach. Intell. 27(2), 162–177 (2005)CrossRefGoogle Scholar
  10. 10.
    Wallbott, H.G.: Bodily expression of emotion. Eur. J. Soc. Psychol. 28, 879–896 (1998)CrossRefGoogle Scholar
  11. 11.
    Wang, J., She, M., Nahavandi, S., Kouzani, A.: A review of vision-based gait recognition methods for human identification. In: International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 320–327 (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Vili Kellokumpu
    • 1
    Email author
  • Markus Särkiniemi
    • 1
  • Guoying Zhao
    • 1
  1. 1.Center for Machine Vision and Signal AnalysisUniversity of OuluOuluFinland

Personalised recommendations