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Content Provider | IET Digital Library |
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Author | Wang, Shuliang Chi, Hehua Yuan, Ziqiang Geng, Jing |
Abstract | 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. |
Starting Page | 470 |
Ending Page | 474 |
Page Count | 5 |
ISSN | 10224653 |
Volume Number | 28 |
e-ISSN | 20755597 |
Issue Number | Issue 3, May (2019) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/cje/28/3 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/cje.2018.09.020 |
Journal | Chinese Journal of Electronics |
Publisher Date | 2019-05-01 |
Access Restriction | Open |
Rights Holder | © Chinese Institute of Electronics |
Subject Keyword | Backward Cloud Generator CK+ Dataset Cloud Computing Cloud Model Common Facial Expression Datasets Common Feature Extraction Computer Vision And Image Processing Technique Emotion Recognition Extended Cohn-Kanade Dataset Face Recognition Feature Extraction Human Beings Human Emotional State Human Emotions Image Recognition Internet Software Japanese Female Facial Expression Machine Learning Pattern Recognition Reliable Emotion Recognition Uncertain Emotional State |
Content Type | Text |
Resource Type | Article |
Subject | Applied Mathematics Electrical and Electronic Engineering |
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