Joint Spatial Geometric and Max-margin Classifier Constraints for Facial Expression Recognition Using Nonnegative Matrix Factorization

International Journal of Machine Learning and Networked Collaborative Engineering

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Field Value
 
Title Joint Spatial Geometric and Max-margin Classifier Constraints for Facial Expression Recognition Using Nonnegative Matrix Factorization
 
Creator Phan, Thanh Trong
Thang, Doan Van
 
Subject facial expressions; classification; nonnegative matrix factorization; graph regularization; spatial constraints
 
Description Based on the constrained non-negative matrix factor algorithm, the article presents a new approach to facial recognition recognition. Our proposed method incorporated two tasks in an automatic expression analysis system: facial feature extraction and classification into expressions. To obtain local and geometric structure information in the data as much as possible, we amalgamate max-margin relegation into the constrained NMF optimization, resulting in a multiplicative updating algorithm is additionally proposed for solving optimization quandary. Experimental results on JAFFE dataset demonstrate that the effectiveness of the proposed method with improved performances over the conventional dimension reduction methods.
 
Publisher SR Informatics, New Delhi, India
 
Date 2019-11-10
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://www.mlnce.net/index.php/Home/article/view/104
 
Source International Journal of Machine Learning and Networked Collaborative Engineering; Vol. 3 No. 03 (2019): Volume No 03, Issue No 03; 125-134
2581-3242
 
Language eng
 
Relation http://www.mlnce.net/index.php/Home/article/view/104/61
 
Rights Copyright (c) 2020 International Journal of Machine Learning and Networked Collaborative Engineering
 

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