Multiparameter Space Decision Voting and Fusion Features for Facial Expression Recognition
Joint Authors
Wang, Yan
Li, Ming
Wan, Xing
Zhang, Congxuan
Wang, Yue
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-12-29
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
Obtaining a valid facial expression recognition (FER) method is still a research hotspot in the artificial intelligence field.
In this paper, we propose a multiparameter fusion feature space and decision voting-based classification for facial expression recognition.
First, the parameter of the fusion feature space is determined according to the cross-validation recognition accuracy of the Multiscale Block Local Binary Pattern Uniform Histogram (MB-LBPUH) descriptor filtering over the training samples.
According to the parameters, we build various fusion feature spaces by employing multiclass linear discriminant analysis (LDA).
In these spaces, fusion features composed of MB-LBPUH and Histogram of Oriented Gradient (HOG) features are used to represent different facial expressions.
Finally, to resolve the inconvenient classifiable pattern problem caused by similar expression classes, a nearest neighbor-based decision voting strategy is designed to predict the classification results.
In experiments with the JAFFE, CK+, and TFEID datasets, the proposed model clearly outperformed existing algorithms.
American Psychological Association (APA)
Wang, Yan& Li, Ming& Wan, Xing& Zhang, Congxuan& Wang, Yue. 2020. Multiparameter Space Decision Voting and Fusion Features for Facial Expression Recognition. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1138946
Modern Language Association (MLA)
Wang, Yan…[et al.]. Multiparameter Space Decision Voting and Fusion Features for Facial Expression Recognition. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-17.
https://search.emarefa.net/detail/BIM-1138946
American Medical Association (AMA)
Wang, Yan& Li, Ming& Wan, Xing& Zhang, Congxuan& Wang, Yue. Multiparameter Space Decision Voting and Fusion Features for Facial Expression Recognition. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1138946
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1138946