K-Means Clustering-Based Kernel Canonical Correlation Analysis for Multimodal Emotion Recognition in Human-Robot Interaction

In this article, K -meansclustering-based Kernel canonical correlation analysis algorithm is proposed for multimodal emotion recognition in human-robot interaction (HRI). The multimodal features (gray pixels; time and frequency domain) extracted from facial expression and speech are fused based on K...

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Veröffentlicht in:	IEEE transactions on industrial electronics (1982) Jg. 70; H. 1; S. 1016 - 1024
Hauptverfasser:	Chen, Luefeng, Wang, Kuanlin, Li, Min, Wu, Min, Pedrycz, Witold, Hirota, Kaoru
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	<named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX"> K</tex-math> </inline-formula> </named-content>-means clustering Algorithms Clustering Clustering algorithms Correlation Correlation analysis Emotion recognition Emotions Feature extraction Feature fusion Histograms Kernel Kernel canonical correlation analysis (KCCA) Kernels multimodal emotion recognition Robots Speech recognition
ISSN:	0278-0046, 1557-9948
Online-Zugang:	Volltext
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