Facial Expression Recognition Algorithm Based on CNN and LBP Feature Fusion

2017 ◽  
Author(s):  
Xinli Yang ◽  
Ming Li ◽  
ShiLin Zhao
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Feature Fusion ◽  
Recognition Algorithm ◽  
Expression Recognition

scholarly journals Facial Expression Recognition Algorithm Based on Fusion of Transformed Multilevel Features and Improved Weighted Voting SVM

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Hao Meng ◽  
Fei Yuan ◽  
Yue Wu ◽  
Tianhao Yan
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Feature Fusion ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Svm Classifier ◽  
Expression Recognition ◽  
Semantic Features ◽  
Weighted Voting ◽  
Single Feature

In allusion to the shortcomings of traditional facial expression recognition (FER) that only uses a single feature and the recognition rate is not high, a FER method based on fusion of transformed multilevel features and improved weighted voting SVM (FTMS) is proposed. The algorithm combines the transformed traditional shallow features and convolutional neural network (CNN) deep semantic features and uses an improved weighted voting method to make a comprehensive decision on the results of the four trained SVM classifiers to obtain the final recognition result. The shallow features include local Gabor features, LBP features, and joint geometric features designed in this study, which are composed of distance and deformation characteristics. The deep feature of CNN is the multilayer feature fusion of CNN proposed in this study. This study also proposes to use a better performance SVM classifier with CNN to replace Softmax since the poor distinction between facial expressions. Experiments on the FERPlus database show that the recognition rate of this method is 17.2% higher than that of the traditional CNN, which proves the effectiveness of the fusion of the multilayer convolutional layer features and SVM. FTMS-based facial expression recognition experiments are carried out on the JAFFE and CK+ datasets. Experimental results show that, compared with the single feature, the proposed algorithm has higher recognition rate and robustness and makes full use of the advantages and characteristics of different features.

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