Consistent Optical Flow Maps for Full and Micro Facial Expression Recognition

Benjamin Allaert, Ioan Marius Bilasco, Chabane Djeraba

Abstract

A wide variety of face models have been used in the recognition of full or micro facial expressions in image sequences. However, the existing methods only address one family of expression at a time, as micro-expressions are quite different from full-expressions in terms of facial movement amplitude and/or texture changes. In this paper we address the detection of micro and full-expression with a common facial model characterizing facial movements by means of consistent Optical Flow estimation. Optical Flow extracted from the face is generally noisy and without specific processing it can hardly cope with expression recognition requirements especially for micro-expressions. Direction and magnitude statistical profiles are jointly analyzed in order to filter out noise and obtain and feed consistent Optical Flows in a face motion model framework. Experiments on CK+ and CASME2 facial expression databases for full and micro expression recognition show the benefits brought by the proposed approach in the filed of facial expression recognition.

References

  1. Bailer, C., Taetz, B., and Stricker, D. (2015). Flow fields: Dense correspondence fields for highly accurate large displacement optical flow estimation. In ICCV.
  2. Chang, C.-C. and Lin, C.-J. (2011). Libsvm: a library for support vector machines. ACM TIST.
  3. Chen, Q. and Koltun, V. (2016). Full flow: Optical flow estimation by global optimization over regular grids. CVPR.
  4. Farnebäck, G. (2003). Two-frame motion estimation based on polynomial expansion. In SCIA. Springer.
  5. Fortun, D., Bouthemy, P., and Kervrann, C. (2015). Optical flow modeling and computation: a survey. Computer Vision and Image Understanding.
  6. Han, S., Meng, Z., Liu, P., and Tong, Y. (2014). Facial grid transformation: A novel face registration approach for improving facial action unit recognition. In ICIP.
  7. Huang, X., Wang, S., Liu, X., Zhao, G., Feng, X., and Pietikainen, M. (2016a). Spontaneous facial micro-expression recognition using discriminative spatiotemporal local binary pattern with an improved integral projection. CVPR.
  8. Huang, X., Zhao, G., Hong, X., Zheng, W., and Pietikäinen, M. (2016b). Spontaneous facial micro-expression analysis using spatiotemporal completed local quantized patterns. Neurocomputing.
  9. Jiang, B., Martinez, B., Valstar, M. F., and Pantic, M. (2014). Decision level fusion of domain specific regions for facial action recognition. In ICPR.
  10. Kazemi, V. and Sullivan, J. (2014). One millisecond face alignment with an ensemble of regression trees. In CVPR.
  11. Lee, C.-S. and Chellappa, R. (2014). Sparse localized facial motion dictionary learning for facial expression recognition. In ICASSP.
  12. Li, X., Pfister, T., Huang, X., Zhao, G., and Pietikäinen, M. (2013). A spontaneous micro-expression database: Inducement, collection and baseline. In FG.
  13. Liao, C.-T., Chuang, H.-J., Duan, C.-H., and Lai, S.-H. (2013). Learning spatial weighting for facial expression analysis via constrained quadratic programming. Pattern Recognition.
  14. Liu, Y.-J., Zhang, J.-K., Yan, W.-J., Wang, S.-J., Zhao, G., and Fu, X. (2015). A main directional mean optical flow feature for spontaneous micro-expression recognition. Affective Computing.
  15. Lucey, P., Cohn, J. F., Kanade, T., Saragih, J., Ambadar, Z., and Matthews, I. (2010). The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression. InCVPR Workshops.
  16. Péteri, R. and Chetverikov, D. (2005). Dynamic texture recognition using normal flow and texture regularity. In IbPRIA.
  17. Revaud, J., Weinzaepfel, P., Harchaoui, Z., and Schmid, C. (2015). Epicflow: Edge-preserving interpolation of correspondences for optical flow. In CVPR.
  18. Su, M.-C., Hsieh, Y., and Huang, D.-Y. (2007). A simple approach to facial expression recognition. In WSEAS.
  19. Wang, S.-J., Yan, W.-J., Zhao, G., Fu, X., and Zhou, C.-G. (2014a). Micro-expression recognition using robust principal component analysis and local spatiotemporal directional features. In ECCV Workshop.
  20. Wang, Y., See, J., Phan, R. C.-W., and Oh, Y.-H. (2014b). Lbp with six intersection points: Reducing redundant information in lbp-top for micro-expression recognition. In ACCV.
  21. Yan, W.-J., Li, X., Wang, S.-J., Zhao, G., Liu, Y.-J., Chen, Y.-H., and Fu, X. (2014). Casme ii: An improved spontaneous micro-expression database and the baseline evaluation. PloS one.
Download


Paper Citation


in Harvard Style

Allaert B., Bilasco I. and Djeraba C. (2017). Consistent Optical Flow Maps for Full and Micro Facial Expression Recognition . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-226-4, pages 235-242. DOI: 10.5220/0006127402350242


in Bibtex Style

@conference{visapp17,
author={Benjamin Allaert and Ioan Marius Bilasco and Chabane Djeraba},
title={Consistent Optical Flow Maps for Full and Micro Facial Expression Recognition},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={235-242},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006127402350242},
isbn={978-989-758-226-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)
TI - Consistent Optical Flow Maps for Full and Micro Facial Expression Recognition
SN - 978-989-758-226-4
AU - Allaert B.
AU - Bilasco I.
AU - Djeraba C.
PY - 2017
SP - 235
EP - 242
DO - 10.5220/0006127402350242