A novel maximum and minimum response-based Gabor (MMRG) feature extraction method for facial expression recognition

2019 ◽  
Vol 78 (16) ◽  
pp. 23369-23397 ◽  
Author(s):  
A. Sherly Alphonse ◽  
M. S. Starvin
Keyword(s):  
Feature Extraction ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Extraction Method ◽  
Expression Recognition ◽  
Feature Extraction Method

A Hierarchical Facial Feature Extraction Method for Expression Recognition

2012 ◽  
Vol 452-453 ◽  
pp. 802-806
Author(s):  
Jin Lin Han ◽  
Hong Zhang
Keyword(s):  
Feature Extraction ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Extraction Method ◽  
Facial Feature ◽  
Support Vector ◽  
Expression Recognition ◽  
Feature Extraction Method ◽  
Facial Identity ◽  
Facial Feature Extraction

With the development of computer visual technology, facial expression recognition plays an important role in the friendly and harmonious human-computer interaction field.Against the inadequacy of the original feature extraction method based on singular value decomposition, this paper proposed a hierarchical facial feature extraction method according to the needs of facial expression recognition, which combines the way of hierarchy and block to enhance the detail information of the image. Then utilize a combination of support vector machine to classify. The results of the two experiments show that the method is effective for the facial identity and expression recognition.

Automatic Facial Expression Recognition by Facial Parts Location with Boosted-LBP

2013 ◽  
pp. 42-55
Author(s):  
Yi Ji ◽  
Khalid Idrissi
Keyword(s):  
Feature Extraction ◽  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Recognition System ◽  
Video Sequences ◽  
Polynomial Kernel ◽  
Expression Recognition ◽  
Emotional States ◽  
Small Set

This paper proposes an automatic facial expression recognition system, which uses new methods in both face detection and feature extraction. In this system, considering that facial expressions are related to a small set of muscles and limited ranges of motions, the facial expressions are recognized by these changes in video sequences. First, the differences between neutral and emotional states are detected. Faces can be automatically located from changing facial organs. Then, LBP features are applied and AdaBoost is used to find the most important features for each expression on essential facial parts. At last, SVM with polynomial kernel is used to classify expressions. The method is evaluated on JAFFE and MMI databases. The performances are better than other automatic or manual annotated systems.

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