SUPPRESSION OF UNCERTAINTIES AT EMOTIONAL TRANSITIONS - Facial Mimics Recognition in Video with 3-D Model

Gerald Krell, Robert Niese, Ayoub Al-Hamadi, Bernd Michaelis

Abstract

Facial expression is of increasing importance for man-machine communication. It is expected that future human computer interaction systems even include emotions of the user. In this work we present an associative approach based on a multi-channel deconvolution for processing of face expression data derived from video sequences supported by a 3-D facial model generated with stereo support. Photogrammetric techniques are applied to determine real world geometric measures and to create a feature vector. Standard classification is used to discriminate between a limited number of mimics, but often fails at transitions from one detected emotion state to another. The proposed associative approach reduces ambiguities at the transitions between different classified emotions. This way, typical patterns of facial expression change is considered.

References

  1. Chang, C.-C. and Lin., C.-J. (2009). Libsvm: a library for support vector machines.
  2. Cohen, I., Sebe, N., Garg, A., Chen, L., and Huang, T. (2003). Facial expression recognition from video sequences: Temporal and static modeling. Computer Vision and Image Understanding, 91(1-2):160-187.
  3. Ekman, P. (1994). Strong evidence for universals in facial expressions: a reply to russell's mistaken critique. Psychol. Bull Journal, pages 268-287.
  4. Fragopanagos, N. and Taylor, J. G. (2005). Emotion recognition in human-computer interaction. Neural Networks, Special Issue, 18:389-405.
  5. Jain, A. (1998). Fundamentals of Digital Image Processing. Prentice Hall. Prentice Hall.
  6. Kohonen, T. (1995). Self-Organizing Maps. Springer.
  7. Li, S. and Jain, A. (2001). Handbook of Face Recognition. Springer.
  8. Niese, R., Al-Hamadi, A., Aziz, F., and Michaelis, B. (2008). Robust facial expression recognition based on 3-d supported feature extraction and svm classification. In Proc. of the IEEE International Conference on Automatic Face and Gesture Recognition (FG2008, Sept. 17-19).
  9. Niese, R., Al-Hamadi, A., Panning, A., Brammen, D., Ebmeyer, U., and Michaelis, B. (2009). Towards pain recognition in post-operative phases using 3dbased features from video and support vector machines. International Journal of Digital Content Technology and its Applications, (in print).
  10. Viola, P. and Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Proceedings of the IEEE Conf. on Computer Vision and Pattern Recognition.
  11. Wu, T. and Lin, C. (2004). Probability estimates for multiclass classification by pair wise coupling. Journal of Machine Learning Research, 5:975-1005.
Download


Paper Citation


in Harvard Style

Krell G., Niese R., Al-Hamadi A. and Michaelis B. (2010). SUPPRESSION OF UNCERTAINTIES AT EMOTIONAL TRANSITIONS - Facial Mimics Recognition in Video with 3-D Model . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010) ISBN 978-989-674-029-0, pages 537-542. DOI: 10.5220/0002852805370542


in Bibtex Style

@conference{visapp10,
author={Gerald Krell and Robert Niese and Ayoub Al-Hamadi and Bernd Michaelis},
title={SUPPRESSION OF UNCERTAINTIES AT EMOTIONAL TRANSITIONS - Facial Mimics Recognition in Video with 3-D Model},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)},
year={2010},
pages={537-542},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002852805370542},
isbn={978-989-674-029-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2010)
TI - SUPPRESSION OF UNCERTAINTIES AT EMOTIONAL TRANSITIONS - Facial Mimics Recognition in Video with 3-D Model
SN - 978-989-674-029-0
AU - Krell G.
AU - Niese R.
AU - Al-Hamadi A.
AU - Michaelis B.
PY - 2010
SP - 537
EP - 542
DO - 10.5220/0002852805370542