Grammatical Facial Expression Recognition Basing on a Hybrid of Fuzzy Rough Ant Colony Optimization and Nearest Neighbor Classifier

2019 ◽  
Author(s):  
Mona Gamal Gafar
Keyword(s):  
Facial Expression ◽  
Ant Colony Optimization ◽  
Facial Expression Recognition ◽  
Nearest Neighbor ◽  
Ant Colony ◽  
Expression Recognition ◽  
Nearest Neighbor Classifier ◽  
Neighbor Classifier

Facial Expression Recognition Based on Gabor Filter Performance Analysis

2013 ◽  
Vol 427-429 ◽  
pp. 1727-1730
Author(s):  
Dong Cheng Shi ◽  
Fang Cai ◽  
Xiao Ding Shi
Keyword(s):  
Facial Expression ◽  
Nearest Neighbor ◽  
Gabor Filter ◽  
Experimental Result ◽  
Center Frequency ◽  
Expression Recognition ◽  
Filter Performance ◽  
Feature Information ◽  
Space Frequency ◽  
Neighbor Classifier

This paper extracts the Gabor phase feature information to classify facial expression. First, preprocessing the image for obtaining the normalization image of pure expression, Gabor transform has good space-frequency localized and multi-directional selectivity, so uses Gabor filter with five frequencies and eight directions to filter the pure expression image. By changing the filter's center frequency, get the optimal image after filtering, and then extract the phase features, carry on the dimension reduction. Finally, with nearest neighbor classifier to classify, a better experimental result had shown in JAFFE database.

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 Features and Fuzzy Classifier

2012 ◽  
Vol 182-183 ◽  
pp. 1046-1050 ◽  
Author(s):  
Xi Bin Jia ◽  
Chun Cheng Wen
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Nearest Neighbor ◽  
Recognition Rate ◽  
Gabor Wavelet ◽  
Expression Recognition ◽  
K Nearest Neighbor ◽  
Fuzzy Classifier ◽  
Gabor Features ◽  
Computational Speed

A novel facial expression recognition method based on Gabor features and fuzzy classifier is proposed. Gabor wavelet is employed for feature extraction because it has good characteristics, which make it very suitable for the area of facial expression recognition. Because high-dimensional Gabor features are quite redundant, DCT and 2DPCA are respectively used to reduce dimensions and select valid features. Finally, expressions are recognized with fuzzy k-nearest neighbor classifier, which is demonstrated to be a more effective classifier. The experimental results show that the proposed method has high computational speed and good recognition rate.

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.

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