A Facial Expression Classification System Based on Active Shape Model and Support Vector Machine

2011 ◽  
Author(s):  
Chen-Chiung Hsieh ◽  
Meng-Kai Jiang
Keyword(s):  
Support Vector Machine ◽  
Facial Expression ◽  
Classification System ◽  
Support Vector ◽  
Active Shape Model ◽  
Shape Model ◽  
Active Shape ◽  
Facial Expression Classification ◽  
Expression Classification

scholarly journals Improved Model for Facial Expression Classification for Fear and Sadness Using Local Binary Pattern Histogram

2020 ◽  
pp. 22-33
Author(s):  
Adebola K. Ojo ◽  
Temitope Ololade Idowu
Keyword(s):  
Feature Extraction ◽  
Facial Expression ◽  
Local Binary Pattern ◽  
Support Vector ◽  
Incremental Development ◽  
Improved Model ◽  
Facial Expression Classification ◽  
Expression Classification ◽  
Accuracy Rates ◽  
Pattern Histogram

In this study, a Local Binary Pattern Histogram model was proposed for Facial expression classification for fear and sadness. There have been a number of supervised machine models developed and used for facial recognition in past researches. The classifier requires human effort to perform feature extraction which has led to unknown changes in the expression of human face and incomplete feature extraction and low accuracy. This study proposed a model for improving the accuracies for fear and sadness and to extract features to distinguish between fear and sadness. Images of different people of varying ages were extracted from two datasets got from Japanese female facial expression (jaffe) dataset and Cohn cade got from Kaggle. In other to achieve an incremental development, classification was done using Linear Support Vector Machine (LSVM) and Random Forest Classifier (RFC). The accuracy rates for the LSVM models, LSVM1 and LSVM2 were 88% and 87% respectively while the RFC models, RFC1 and RFC2, were 81% and 82% respectively.

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