Discriminant Analysis of Two-Dimensional Gabor Features for Facial Expression Recognition

2012 ◽  
Vol 46 (3) ◽  
pp. 89-92 ◽  
Author(s):  
L. Kh. Kashapova ◽  
E. Yu. Latysheva ◽  
I. N. Spiridonov
Keyword(s):  
Discriminant Analysis ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Two Dimensional ◽  
Gabor Features

Facial Expression Recognition Based on Multi-Channel Fusion of Gabor Features

2013 ◽  
Vol 427-429 ◽  
pp. 1963-1967 ◽  
Author(s):  
Shu Yi Wang ◽  
Jing Ling Wang ◽  
Chuan Zhen Li
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Nearest Neighbor ◽  
Recognition Algorithm ◽  
Gabor Wavelet ◽  
High Rate ◽  
Expression Recognition ◽  
Channel Integration ◽  
Gabor Features ◽  
Gabor Feature

This paper presents a facial expression recognition algorithm based on multi-channel integration of Gabor feature. First, a Gabor wavelet filter extracts facial features with 5 scales and 8 orientations, and then transform the 40 channels into 13 channels according to the maximum rule presented in this paper. Second, we reduce the dimension of expression features by the method of PCA+LDA. At last, expression features are classified using the nearest neighbor method. The experiments involve two databases and show that the proposed algorithm can recognize facial expression in high rate.

Facial Expression Recognition Algorithm Based on Gabor Texture Features and Adaboost Feature Selection via Sparse Representation

2014 ◽  
Vol 511-512 ◽  
pp. 433-436 ◽  
Author(s):  
Qing Wei Wang ◽  
Zi Lu Ying
Keyword(s):  
Feature Selection ◽  
Facial Expression ◽  
Sparse Representation ◽  
Facial Expression Recognition ◽  
Texture Features ◽  
Recognition Algorithm ◽  
Gabor Wavelet ◽  
Expression Recognition ◽  
Adaboost Algorithm ◽  
Gabor Features

This paper proposed a new facial expression recognition algorithm based on gabor texture features and Adaboost feature selection via SRC(sparse representation classification). Five scales and eight orientations of Gabor wavelet filters were used in this paper to extract gabor features. For an image of size , the number of gabor features is 163840, In order to extract the most effective features for FER(facial expression recognition), Adaboost algorithm is used for feature selection. This paper divided 7 facial expressions into two categories, where the neutral expression as the first class and the remaining six expressions as the second class. In each size and orientation 110 features are selected. At last 4400 features are selected combined SRC algorithm for FER. Test experiments were performed on Japanese female JAFFE facial expression database. Compared with the traditional expression recognition algorithms such as 2DPCA+SVM, LDA+SVM, the new algorithm achieved a better recognition rate, which shows the effectiveness of the proposed new algorithm.

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