Abstract
The automated and exact identification of facial expressions in human computer interaction scenarios is a challenging but necessary task to recognize human emotions by a machine learning system. The human face consists of regions whose elements contribute to single expressions in a different manner. This work aims to shed light onto the importance of specific facial regions to provide information which can be used to discriminate between different facial expressions from a statistical pattern recognition perspective. A sampling based classification approach is used to reveal informative locations in the face. The results are expression-sensitive importance maps that indicate regions of high discriminative power which can be used for various applications.
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Acknowledgements
This paper is based on work done within the Transregional Collaborative Research Centre SFB/TRR 62 Companion-Technology for Cognitive Technical Systems funded by the German Research Foundation (DFG). Markus Kächele is supported by a scholarship of the Landesgraduiertenförderung Baden-Württemberg at Ulm University.
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Kächele, M., Palm, G., Schwenker, F. (2015). Monte Carlo Based Importance Estimation of Localized Feature Descriptors for the Recognition of Facial Expressions. In: Schwenker, F., Scherer, S., Morency, LP. (eds) Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction. MPRSS 2014. Lecture Notes in Computer Science(), vol 8869. Springer, Cham. https://doi.org/10.1007/978-3-319-14899-1_4
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