Sparse Facial Expression Recognition Algorithm Based on Integrated Gabor Feature

2015 ◽  
pp. 401-408
Author(s):  
Yang Liu ◽  
Lei Ren ◽  
Hong Shao
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition Algorithm ◽  
Expression Recognition ◽  
Gabor Feature

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 Based on Gabor Texture Features and Centre Binary Pattern

2015 ◽  
Vol 742 ◽  
pp. 257-260 ◽  
Author(s):  
Li Sai Li ◽  
Zi Lu Ying ◽  
Bin Bin Huang
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Texture Feature ◽  
Texture Features ◽  
Gabor Wavelet ◽  
Expression Recognition ◽  
Wavelet Filters ◽  
Adaboost Algorithm ◽  
Gabor Feature

This paper was proposed a new algorithm for Facial Expression Recognition (FER) which was based on fusion of gabor texture features and Centre Binary Pattern (CBP). Firstly, gabor texture feature were extracted from every expression image. Five scales and eight orientations of gabor wavelet filters were used to extract gabor texture features. Then the CBP features were extracted from gabor feature images and adaboost algorithm was used to select final features from CBP feature images. Finally, we obtain expression recognition results on the final expression features by Sparse Representation-based Classification (SRC) method. The experiment results on Japanese Female Facial Expression (JAFFE) database demonstrated that the new algorithm had a much higher recognition rate than the traditional algorithms.

Sign in / Sign up

Export Citation Format

Share Document