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Framework for Combination Aware AU Intensity Recognition Host Publication: IEEE 6th International Conference on Affective Computing and Intelligent Interaction Authors: I. Gonzalez, M. Oveneke, D. Jiang, W. Verhelst and H. Sahli Publication Year: 2015 Number of Pages: 608
Abstract: We present a framework for combination aware AUintensity recognition. It includes a feature extraction approachthat can handle small head movements which does not requireface alignment. A three layered structure is used for the AUclassification. The first layer is dedicated to independent AU recognition, and the second layer incorporates AU combinationknowledge. At a third layer, AU dynamics are handled based onvariable duration semi-Markov model. The first two layers aremodeled using extreme learning machines (ELMs). ELMs haveequal performance to support vector machines but are computationallymore efficient, and can handle multi-class classificationdirectly. Moreover, they include feature selection via manifoldregularization. We show that the proposed layered classificationscheme can improve results by considering AU combinations aswell as intensity recognition.
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