A comparison study of feature spaces and classification methods for facial expression recognition

2013 ◽  
Author(s):  
Chun Fui Liew ◽  
Takehisa Yairi
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Classification Methods ◽  
Comparison Study ◽  
Feature Spaces

scholarly journals Multiparameter Space Decision Voting and Fusion Features for Facial Expression Recognition

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Yan Wang ◽  
Ming Li ◽  
Xing Wan ◽  
Congxuan Zhang ◽  
Yue Wang
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Nearest Neighbor ◽  
Feature Space ◽  
Expression Recognition ◽  
Linear Discriminant ◽  
Training Samples ◽  
Feature Spaces ◽  
Fusion Feature ◽  
Fusion Features

Obtaining a valid facial expression recognition (FER) method is still a research hotspot in the artificial intelligence field. In this paper, we propose a multiparameter fusion feature space and decision voting-based classification for facial expression recognition. First, the parameter of the fusion feature space is determined according to the cross-validation recognition accuracy of the Multiscale Block Local Binary Pattern Uniform Histogram (MB-LBPUH) descriptor filtering over the training samples. According to the parameters, we build various fusion feature spaces by employing multiclass linear discriminant analysis (LDA). In these spaces, fusion features composed of MB-LBPUH and Histogram of Oriented Gradient (HOG) features are used to represent different facial expressions. Finally, to resolve the inconvenient classifiable pattern problem caused by similar expression classes, a nearest neighbor-based decision voting strategy is designed to predict the classification results. In experiments with the JAFFE, CK+, and TFEID datasets, the proposed model clearly outperformed existing algorithms.

scholarly journals Facial Expression Recognition using Deep Learning

2020 ◽  
Vol 9 (4) ◽  
pp. 1845-1849
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Data Sets ◽  
Expression Recognition ◽  
Distribution Analysis ◽  
Classification Methods ◽  
Data Set ◽  
Facial Expression Analysis ◽  
Recognition Systems

Facial expression recognition (FER) is now getting extensively popular because of its ability to predict an unknown data-set, and to its extent with some accuracy. An average human being possesses or shows seven different expressions based on the situation, namely anger, sad, happy, surprise, disgust, neutral and scared. Each individual has a unique way to express the afore-mentioned expressions and hence the term “an unknown data-set”. To identify human’s present mindset through facial expressions, many data sets are prepared based on face components (such as lips, cheek, nose, eyes and eye brows etc.,) dislocations and elasticity of all the facial parts. Many facial recognition systems are functioning on muscle distribution analysis from the mother image set’s pixel parameters. This research paper is going to present about image pre processing, facial expression learning methods, classification methods and implementation of FaceEx algorithm for facial expression analysis through FER2013 CNN data sets and Viola-Jones Principle.

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