Graph Laplacian-Improved Convolutional Residual Autoencoder for Unsupervised Human Action and Emotion Recognition

This paper presents an unsupervised feature learning approach based on 3D-skeleton data for human action and human discrete emotion recognition. Relying on the time series of skeleton data analysis to perform such tasks is effective and important to preserve the individual's privacy better. Bes...

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Veröffentlicht in:	IEEE access Jg. 10; S. 1
Hauptverfasser:	Paoletti, Giancarlo, Beyan, Cigdem, Del Bue, Alessio
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.01.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Action recognition autoencoder Computing time Data analysis Domains Emotion recognition Emotions full-body movement graph Laplacian Joints (anatomy) Laplace equations Machine learning Object recognition Protocols Regularization Representation learning skeletal data Skeleton Task analysis Training unsupervised feature learning
ISSN:	2169-3536, 2169-3536
Online-Zugang:	Volltext
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