Dynamic Facial Expression Feature Extraction and Classification Based on Candide-3 Face Model

2014 ◽  
Author(s):  
Dong Li ◽  
Xinzhu Wang ◽  
Yantao Tian
Keyword(s):  
Feature Extraction ◽  
Facial Expression ◽  
Face Model ◽  
Dynamic Facial Expression

Optimization of Feature Extraction Filter for Facial Expression Utilizing GP

2012 ◽  
Vol 132 (10) ◽  
pp. 1656-1666
Author(s):  
Koki Matsumura ◽  
Shatoshi Shinmei ◽  
Shuhei Kimura
Keyword(s):  
Feature Extraction ◽  
Facial Expression

scholarly journals Dynamic Facial Expression Understanding Using Deep Spatiotemporal LDSP On Spark

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 16866-16877
Author(s):  
Md Azher Uddin ◽  
Joolekha Bibi Joolee ◽  
Kyung-Ah Sohn
Keyword(s):  
Facial Expression ◽  
Dynamic Facial Expression

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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