scholarly journals Facial Expression Recognition Using 3D Facial Feature Distances

10.5772/6189 ◽  
2008 ◽  
Author(s):  
Hamit Soyel ◽  
Hasan Demirel
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Facial Feature ◽  
Expression Recognition

Research on the Facial Expression Feature Extraction of Facial Expression Recognition Based on MATLAB

2014 ◽  
Vol 1049-1050 ◽  
pp. 1522-1525
Author(s):  
Wang Ju ◽  
Ding Rui ◽  
Chun Yan Nie
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Facial Expression ◽  
Interpersonal Communication ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Facial Feature ◽  
Expression Recognition ◽  
Information Communication ◽  
Facial Feature Extraction

In such a developed day of information communication, communication is an important essential way of interpersonal communication. As a carrier of information, expression is rich in human behavior information. Facial expression recognition is a combination of many fields, but also a new topic in the field of pattern recognition. This paper mainly studied the facial feature extraction based on MATLAB, by MATLAB software, extracting the expression features through a large number of facial expressions, which can be divided into different facial expressions more accurate classification .

scholarly journals EKSTRAKSI FITUR BERBASIS AVERAGE FACE UNTUK PENGENALAN EKSPRESI WAJAH

2017 ◽  
Vol 2 (2) ◽  
pp. 130-134
Author(s):  
Jarot Dwi Prasetyo ◽  
Zaehol Fatah ◽  
Taufik Saleh
Keyword(s):  
Machine Learning ◽  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Facial Feature ◽  
Expression Recognition ◽  
Facial Feature Extraction ◽  
Sample Data ◽  
Machine Interface ◽  
The Difference

In recent years it appears interest in the interaction between humans and computers. Facial expressions play a fundamental role in social interaction with other humans. In two human communications is only 7% of communication due to language linguistic message, 38% due to paralanguage, while 55% through facial expressions. Therefore, to facilitate human machine interface more friendly on multimedia products, the facial expression recognition on interface very helpful in interacting comfort. One of the steps that affect the facial expression recognition is the accuracy in facial feature extraction. Several approaches to facial expression recognition in its extraction does not consider the dimensions of the data as input features of machine learning Through this research proposes a wavelet algorithm used to reduce the dimension of data features. Data features are then classified using SVM-multiclass machine learning to determine the difference of six facial expressions are anger, hatred, fear of happy, sad, and surprised Jaffe found in the database. Generating classification obtained 81.42% of the 208 sample data.

Sign in / Sign up

Export Citation Format

Share Document