Abstract
We present a system for classification of nine voluntary facial actions, i.e. Neutral, Smile, Sad, Surprise, Angry, Speak, Blink, Left, and Right. The data is assessed by an Emotiv EPOC wireless EEG head-set. We derive spectral features and step function features that represent the main signal characteristics of the recorded data in a straightforward manner. With a two stage classification setup using support vector machines we achieve an overall recognition accuracy of 81.8%. Furthermore, we show a qualitative evaluation of an online system for facial action recognition using the EPOC device.
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References
Emotiv Software Development Kit User Manual for Release 1.0.0.4
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Heger, D., Putze, F., Schultz, T. (2011). Online Recognition of Facial Actions for Natural EEG-Based BCI Applications. In: D’Mello, S., Graesser, A., Schuller, B., Martin, JC. (eds) Affective Computing and Intelligent Interaction. ACII 2011. Lecture Notes in Computer Science, vol 6975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24571-8_56
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DOI: https://doi.org/10.1007/978-3-642-24571-8_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24570-1
Online ISBN: 978-3-642-24571-8
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