Abstract
In this paper we investigate the combination of shape features and Phase-based Gabor features for context-independent Action Unit Recognition. For our recognition goal, three regions of interest have been devised that efficiently capture the AUs activation/deactivation areas. In each of these regions a feature set consisting of geometrical and histogram of Gabor phase appearance-based features have been estimated. For each Action Unit, we applied Adaboost for feature selection, and used a binary SVM for context-independent classification. Using the Cohn-Kanade database, we achieved an average F 1 score of 93.8% and an average area under the ROC curve of 97.9 %, for the 11 AUs considered.
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Gonzalez, I., Sahli, H., Enescu, V., Verhelst, W. (2011). Context-Independent Facial Action Unit Recognition Using Shape and Gabor Phase Information. In: D’Mello, S., Graesser, A., Schuller, B., Martin, JC. (eds) Affective Computing and Intelligent Interaction. ACII 2011. Lecture Notes in Computer Science, vol 6974. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24600-5_58
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DOI: https://doi.org/10.1007/978-3-642-24600-5_58
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24599-2
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