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Erschienen in:
2018 | OriginalPaper | Buchkapitel
Identification of Multimodal Signals for Emotion Recognition in the Context of Human-Robot Interaction
verfasst von : Andrea K. Pérez, Carlos A. Quintero, Saith Rodríguez, Eyberth Rojas, Oswaldo Peña, Fernando De La Rosa
Erschienen in: Intelligent Computing Systems
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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 human-robot 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.