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Emotion Recognition Using Cloud Model

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  • NETL
  • NSTL
  • 万方数据
  • 维普
Emotions often facilitate interactions among human beings, but the big variation of human emotional states make a negative effect on the reliable emotion recognition. We propose a novel algorithm to extract common features for each type of emotional states which can reliably present human emotions. To uncover the common features from uncertain emotional states, the backward cloud generator is used to discover {Ex, En, He} by integrating randomness and fuzziness. Finally, the proposed method for emotion recognition is verified on the common facial expression datasets, the Extended Cohn-Kanade (CK+) dataset and the Japanese female facial expression (JAFFE). The results are satisfactory, which shows cloud model is potentially useful in pattern recognition and machines learning.

Cloud modelFeature extractionHuman-machine interactionEmotion recognition

WANG Shuliang、CHI Hehua、YUAN Ziqiang、GENG Jing

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School of Computer Science, Wuhan University, Wuhan 430079, China

School of software, Beijing Institute of Technology, Beijing 100081, China

Goergen Institute for Data Science, University of Rochester, Rochester NY 14627, USA

National Key Research and Development Plan of ChinaNational Key Research and Development Plan of ChinaNational Natural Science Fund of China

2016YFC08030002016YFB050260461472039

2019

10.1049/cje.2018.09.020

中国电子杂志(英文版)

中国电子杂志(英文版)

CSTPCDCSCDSCIEI
ISSN：1022-4653
年,卷(期)：2019.28(3)
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