Facial expression recognition based on fusion feature of PCA and LBP with SVM

Optik ◽  
2013 ◽  
Vol 124 (17) ◽  
pp. 2767-2770 ◽  
Author(s):  
Yuan Luo ◽  
Cai-ming Wu ◽  
Yi Zhang
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Fusion Feature

Weighted-fusion feature of MB-LBPUH and HOG for facial expression recognition

2019 ◽  
Vol 24 (8) ◽  
pp. 5859-5875 ◽  
Author(s):  
Yan Wang ◽  
Ming Li ◽  
Congxuan Zhang ◽  
Hao Chen ◽  
Yuming Lu
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Weighted Fusion ◽  
Fusion Feature

scholarly journals Multiparameter Space Decision Voting and Fusion Features for Facial Expression Recognition

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Yan Wang ◽  
Ming Li ◽  
Xing Wan ◽  
Congxuan Zhang ◽  
Yue Wang
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Nearest Neighbor ◽  
Feature Space ◽  
Expression Recognition ◽  
Linear Discriminant ◽  
Training Samples ◽  
Feature Spaces ◽  
Fusion Feature ◽  
Fusion Features

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.

Graph-based dynamic ensemble pruning for facial expression recognition

2019 ◽  
Vol 49 (9) ◽  
pp. 3188-3206 ◽  
Author(s):  
Danyang Li ◽  
Guihua Wen ◽  
Xu Li ◽  
Xianfa Cai
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Ensemble Pruning
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