Abstract
This article presents a feature-based framework to automatically track 18 facial landmarks for emotion recognition and emotional dynamic analysis. With a new way of using multi-kernel learning, we combine two methods: the first matches facial feature points between consecutive images and the second uses an offline learning of the facial landmark appearance. Matching points results in a jitter-free tracking and the offline learning prevents the tracking framework from drifting. We train the tracking system on the Cohn-Kanade database and analyze the dynamic of emotions and Action Units on the MMI database sequences. We perform accurate detection of facial expressions temporal segment and report experimental results.
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Senechal, T., Rapp, V., Prevost, L. (2011). Facial Feature Tracking for Emotional Dynamic Analysis. In: Blanc-Talon, J., Kleihorst, R., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2011. Lecture Notes in Computer Science, vol 6915. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23687-7_45
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DOI: https://doi.org/10.1007/978-3-642-23687-7_45
Publisher Name: Springer, Berlin, Heidelberg
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