Abstract
The analysis of facial expression temporal dynamics is of great importance for many real-world applications. Furthermore, due to the variability among individuals and different contexts, the dynamic relationships among facial features are stochastic. Systematically capturing such temporal dependencies among facial features and incorporating them into the facial expression recognition process is especially important for interpretation and understanding of facial behaviors. The base system in this paper uses Hidden Markov Models (HMMs) and a new set of derived features from geometrical distances obtained from detected and automatically tracked facial points. We propose here to transform numerical representation which is in the form of multi time series to a symbolic representation in order to reduce dimensionality, extract the most pertinent information and give a meaningful representation to human. Experiments show that new and interesting results have been obtained from the proposed approach.
The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-02895-8_64
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Ghanem, K. (2013). Hidden Markov Models for Modeling Occurrence Order of Facial Temporal Dynamics. In: Blanc-Talon, J., Kasinski, A., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2013. Lecture Notes in Computer Science, vol 8192. Springer, Cham. https://doi.org/10.1007/978-3-319-02895-8_16
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DOI: https://doi.org/10.1007/978-3-319-02895-8_16
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