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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Vydáno v:	IEEE transactions on industrial electronics (1982) Ročník 70; číslo 1; s. 1016 - 1024
Hlavní autoři:	Chen, Luefeng, Wang, Kuanlin, Li, Min, Wu, Min, Pedrycz, Witold, Hirota, Kaoru
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	<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
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