Abstract
This paper is concerned with capturing the dynamics of facial expression. The dynamics of facial expression can be described as the intensity and timing of a facial expression and its formation. To achieve this we developed a technique that can accurately classify and differentiate between subtle and similar expressions, involving the lower face. This is achieved by using Local Linear Embedding (LLE) to reduce the dimensionality of the dataset and applying Support Vector Machines (SVMs) to classify expressions. We then extended this technique to estimate the dynamics of facial expression formation in terms of intensity and timing.
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References
Darwin, C., Ekman, P.: The expression of the emotions in man and animal. The University of Chicago Press, Chicago (1998); 1st edition in 1872, 2nd edition in 1889, 3rd edition with additional commentry by Ekman, P. (1998)
Cohn, J., Kanade, T., Moriyama, T., Ambadar, Z., Xiao, J., Gao, J., Imamura, H.: A comparative study of alternative facs coding algorithms. Technical Report CMU-RI-TR-02-06, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA (2001)
Ekman, P., Friesen, W., Hager, J.: Facial action coding system. Consulting Psychologists Press, Palo Alto (1978)
Cohn, J.F., Schmidt, K., Gross, R., Ekman, P.: Individual differences in facial expression: Stability over time, relation to self-reported emotion, and ability to inform person identification. In: Proceedings of Intel. Conf. On Multimedia and Expo., 2001 (2002)
Ambadar, Z., Schooler, J., Cohn, J.: Deciphering the enigmatic face: The importance of facial dynamics to interpreting subtle facial expressions. Psychological Science (2005)
Bartlett, M.S., Movellan, J., Littlewort, G., Braathen, B., Frank, M.G., Sejnowski, T.J.: Towards automatic recognition of spontaneous facial actions. In: Ekman, P. (ed.) What the face reveals. Oxford University Press, Oxford (2003)
Cohen, I., Sebe, N., Huang, L.C.A.G.: Facial expression recognition from video sequences: Temporal and static modeling. Computer Vision and Image Understanding, 91(1-2) (2003)
Ekman, P.: Facial expression of emotion: New findings, new questions (1992)
Hadid, A., Pietikdinen, M.: An experimental investigation about the integration of facial dynamics in video-based face recognition. ELCVIA 5, 1–13 (2005)
Littlewort, G., Bartlett, M.S., Fasel, I., Susskind, J., Movellan, J.: Dynamics of facial expression extracted automatically from video. Computer vision and Image understanding (2006)
Ekman, P., Friesen, W., Hager, J.: Facial Action Coding System Manual (2002)
Ghent, J., McDonald, J., Harper, J.: Statistical model for expression generation using the facial action coding system. Technical Report NUIM-CS-TR-2003-01, NUI Maynooth (2003)
Saul, L.K., Roweis, S.T.: An introduction to locally linear embedding (2001), http://www.cs.toronto.edu/roweis/lle/publications.html
Schmidt, K., Cohn, J.: Dynamics of facial expression: Normative characteristics and individual differences. In: IEEE International Conference on Multimedia and Expo (ICME 2001), pp. 728–731 (2001)
Cohn, J.: Automated analysis of the configuration and timing of facial expression. In: Ekman, P., Rosenberg, E. (eds.) Afterword of What the face reveals (2nd edn.): Basic and applied studies of spontaneous expression using the Facial Action Coding System (FACS) (2005)
Pantic, M., Patras, I.: Dynamics of facial expression: Recognition of facial actions and their temporal segments from face profile image sequences. SMC-B 36, 433–449 (2006)
Zhang, Y., Ji, Q.: Facial expression understanding in image sequences using dynamic and active visual information fusion. In: ICCV, pp. 113–118 (2003)
Donato, G., Bartlett, M., Hager, J., Ekman, P., Sejnowski, T.: Classifying facial actions. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(10), 974–989 (1999)
Bartlett, M.S., Littlewort, G., Frank, M., lainscsek, C., Fasel, I., Movellan, J.: Recognising facial expression: Machine learning and application to spontaneous behaviour. In: IEEE International conference on computer vision and pattern recognition (2005)
Bartlett, M.S., Littlewort, G., Lainscsek, C., Fasel, I., Movellan, J.: Machine learning methods for fully automatic recognition of facial expressions and facial actions. In: IEEE International conference on systems, man and cybernetics, pp. 592–597 (2004)
Bartlett, M.S., Braathen, B., Movellan, J.: Automatic analysis of spontaneous facial behavior: A final report. MPLAB-TR-2001-06, Institute for Neural Computation, University of California, San Diego (2001)
Bartlett, M.S., Littlewort, G., Fasel, I., Movellan, J.: Real-time face detection and facial expression recognition: Development and applications to human computer interaction (2003)
Bartlett, M.S., Littlewort, G., Braathen, B., Sejnowski, T., Movellan, J.: A prototype for automatic recognition of spontaneous facial actions (2003)
Bartlet, M., Littlewort, G., Lainscsek, C., Fasel, I., Frank, M., Movellan, J.: Fully automatic facial action recognition in spontaneous behavior bartlett (2006)
Goneid, A., el Kaliouby, R.: Facial feature analysis of spontaneous facial expression. In: Proceedings of the 10th International AI Applications Conference (2002)
Fasel, B., Luettin, J.: Automatic facial expression analysis: A survey. Pattern Recognition 36(1), 259–275 (2003)
Saul, L.K., Roweis, S.T.: Think globally, fit locally: unsupervised learning of low dimensional manifolds. Journal of Machine Learning Research 4 (2003)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by local linear embedding. Science 290, 2323–2326 (2000)
Chang, Y., Yeung, D.Y.: Robust local linear embedding. Technical report, Department of Computer Science, Hong Kong University of Science and Technology, HKUST-CS05-12 (2005)
Hadid, A., PietikAanien, M.: Efficient locally linear embeddings of imperfect manifolds. In: Proceedings of the Third International Conference on Machine Learning and Data Mining in Pattern Recognition, Leipzig, Germany, pp. 188–201 (2003)
Ridder, D.D., Kouropteva, O., Okun, O., PietikAanien, M., Duin, R.P.W.: Supervised locally linear embedding. In: ICANN 2003, pp. 333–341 (2003)
Kouropteva, O., Okun, O., Hadid, A., Soriano, M., Marcos, S., PietikAainen, M.: Beyond locally linear embedding algorithm. Technical report, Department of Electrical and Information Engerring, University of Oulu, Oulu, Finland, MVG-01-2002 (2002)
Ridder, D.D., Duin, R.P.W.: Locally linear embedding for classification. Technical report, Pattern Recognition Group, Department of Imaging Science and Technology, Delft University of Technology, Delft, The Netherlands, PH-2002-01 (2002)
Campbell, C.: kernel methods: A survey of current techniques. Neurocomputing 48, 63–84 (2002)
Rogers, S., Williams, R.D., Campbell, C.: Class prediction with Microarray Datasets. In: BioInformatics with computational intelligence paradigms. Springer, Heidelberg (2004)
Rogers, S.: Machine Learning Techniques for Microarray Analysis. Faculty of engineering mathematics, University of Bristol (2004)
ScholKopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2002)
Ghent, J.: A Computational Model of Facial Expression. PhD thesis, National University of Ireland Maynooth, Co., kildare, Ireland (2005)
Cohn, J., Kanade: Cohn-kanade au-coded facial expression database. Technical report, Pittsburgh University (1999)
Gower, J.C.: Generalised procrustes analysis. Psychometrika 40, 33–50 (1975)
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Reilly, J., Ghent, J., McDonald, J. (2006). Investigating the Dynamics of Facial Expression. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2006. Lecture Notes in Computer Science, vol 4292. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11919629_35
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DOI: https://doi.org/10.1007/11919629_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-48626-8
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