scholarly journals Facial Expression Recognition using Feature Extraction based on Estimation of Useful Facial Features

2011 ◽  
Vol 23 (2) ◽  
pp. 170-185 ◽  
Author(s):  
Hiroki NOMIYA ◽  
Teruhisa HOCHIN
Keyword(s):  
Feature Extraction ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Facial Features ◽  
Expression Recognition

scholarly journals Facial Expression Analysis by Machine Learning

2010 ◽  
pp. 239-258
Author(s):  
Siu-Yeung Cho ◽  
Teik-Toe Teoh ◽  
Yok-Yen Nguwi
Keyword(s):  
Feature Selection ◽  
Facial Expression ◽  
Real Time ◽  
Facial Expression Recognition ◽  
Classification Performance ◽  
Difficult Problem ◽  
Facial Features ◽  
Expression Recognition ◽  
Real Time Processing ◽  
Good Classification Performance

Facial expression recognition is a challenging task. A facial expression is formed by contracting or relaxing different facial muscles on human face that results in temporally deformed facial features like wide-open mouth, raising eyebrows or etc. The challenges of such system have to address with some issues. For instances, lighting condition is a very difficult problem to constraint and regulate. On the other hand, real-time processing is also a challenging problem since there are so many facial features to be extracted and processed and sometimes, conventional classifiers are not even effective in handling those features and produce good classification performance. This chapter discusses the issues on how the advanced feature selection techniques together with good classifiers can play a vital important role of real-time facial expression recognition. Several feature selection methods and classifiers are discussed and their evaluations for real-time facial expression recognition are presented in this chapter. The content of this chapter is a way to open-up a discussion about building a real-time system to read and respond to the emotions of people from facial expressions.

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.

A Hybrid Approach for Facial Expression Recognition Using Extended Local Binary Patterns and Principal Component Analysis

2019 ◽  
Vol 8 (2) ◽  
pp. 1-25
Author(s):  
Gopal Krishan Prajapat ◽  
Rakesh Kumar
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Local Binary Pattern ◽  
Recognition Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Expression Recognition ◽  
Distance Method

Facial feature extraction and recognition plays a prominent role in human non-verbal interaction and it is one of the crucial factors among pose, speech, facial expression, behaviour and actions which are used in conveying information about the intentions and emotions of a human being. In this article an extended local binary pattern is used for the feature extraction process and a principal component analysis (PCA) is used for dimensionality reduction. The projections of the sample and model images are calculated and compared by Euclidean distance method. The combination of extended local binary pattern and PCA (ELBP+PCA) improves the accuracy of the recognition rate and also diminishes the evaluation complexity. The evaluation of proposed facial expression recognition approach will focus on the performance of the recognition rate. A series of tests are performed for the validation of algorithms and to compare the accuracy of the methods on the JAFFE, Extended Cohn-Kanade images database.

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