Identification of multimodal signals for emotion recognition in the context of human-robot interaction
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2018-02-17
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Abstract
This paper presents a proposal for the identification of multimodal
signals for recognizing 4 human emotions in the context of humanrobot
interaction, specifically, the following emotions: happiness, anger,
surprise and neutrality. We propose to implement a multiclass classifier
that is based on two unimodal classifiers: one to process the input
data from a video signal and another one that uses audio. On one hand,
for detecting the human emotions using video data we have propose
a multiclass image classifier based on a convolutional neural network
that achieved 86.4% of generalization accuracy for individual frames and
100% when used to detect emotions in a video stream. On the other
hand, for the emotion detection using audio data we have proposed a
multiclass classifier based on several one-class classifiers, one for each
emotion, achieving a generalization accuracy of 69.7%. The complete
system shows a generalization error of 0% and is tested with several real
users in an sales-robot application.
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Atribución-NoComercial-CompartirIgual 2.5 Colombia