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.

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.

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 WAVELET TRANSFORMATION AND ELASTIC TEMPLATES MATCHING

2006 ◽  
Vol 06 (01) ◽  
pp. 125-138 ◽  
Author(s):  
YONGZHAO ZHAN ◽  
JINGFU YE ◽  
DEJIAO NIU ◽  
PENG CAO
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Wavelet Transformation ◽  
Recognition Rate ◽  
Image Understanding ◽  
Gabor Wavelet ◽  
Expression Recognition ◽  
K Nearest Neighbors ◽  
Matching Algorithm ◽  
Gabor Wavelet Transformation

Facial expression recognition technology plays an important role in research areas such as psychological studies, image understanding and virtual reality etc. In order to achieve subject-independent facial expression recognition and obtain robustness against illumination variety and image deformation, facial expression recognition methods based on Gabor wavelet transformation and elastic templates matching are presented in this paper. First given a still image containing facial expression information, preprocessors are executed which include gray and scale normalization. Secondly, Gabor wavelet filters are adopted to extract expression features. Then the elastic graph for expression features is constructed. Finally, elastic templates matching algorithm and K-nearest neighbors classifier are used to recognize facial expression. Experiments show that expression features can be extracted effectively by Gabor wavelet transformation, which is insensitive to illumination variety and individual difference, and high recognition rate can be obtained using elastic templates matching algorithm, which is subject-independent.

Facial Expression Recognition Based on Improved Dimension Reduction of Gabor Feature and Two-against-Two Multi-Class SVM Classification

2013 ◽  
Vol 373-375 ◽  
pp. 654-659
Author(s):  
Jin Xin Ruan ◽  
Li Ying Xie ◽  
Jun Xun Yin
Keyword(s):  
Facial Expression ◽  
Dimension Reduction ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Expression Recognition ◽  
Accurate Expression ◽  
Gabor Feature ◽  
Feature Dimension

The facial expression recognition technology has been widespread concerned and researched, and many methods have been presented. This paper focuses on studying and analyzing the feature extraction, feature dimension reduction and two-against-two multi-class Support Vector Machine (SVM) method, and an algorithm is proposed for recognition of six basic facial expressions. According to expression feature information in the different face region, the algorithm adopts local nonuniform feature point extraction to reduce the feature dimension. After transforming the feature points with Gabor filters, the Gabor expression features are obtained. And the feature dimension is further reduced by discrete wavelet transform (DWT) and discrete cosine transform (DCT). At last, the tow-against-two classification method and an optimum decision scheme are used to realize quick and accurate expression classification. Experimental results show the algorithm can achieve higher recognition rate, recognition speed and stronger robust.

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.

FEATURE-BASED FACIAL EXPRESSION RECOGNITION: SENSITIVITY ANALYSIS AND EXPERIMENTS WITH A MULTILAYER PERCEPTRON

1999 ◽  
Vol 13 (06) ◽  
pp. 893-911 ◽  
Author(s):  
ZHENGYOU ZHANG
Keyword(s):  
Sensitivity Analysis ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Low Frequency ◽  
Gabor Wavelet ◽  
Expression Recognition ◽  
Wavelet Coefficients ◽  
Feature Based

In this paper, we report our experiments on feature-based facial expression recognition within an architecture based on a two-layer perceptron. We investigate the use of two types of features extracted from face images: the geometric positions of a set of fiducial points on a face, and a set of multiscale and multiorientation Gabor wavelet coefficients at these points. They can be used either independently or jointly. The recognition performance with different types of features has been compared, which shows that Gabor wavelet coefficients are much more powerful than geometric positions. Furthermore, since the first layer of the perceptron actually performs a nonlinear reduction of the dimensionality of the feature space, we have also studied the desired number of hidden units, i.e. the appropriate dimension to represent a facial expression in order to achieve a good recognition rate. It turns out that five to seven hidden units are probably enough to represent the space of facial expressions. Then, we have investigated the importance of each individual fiducial point to facial expression recognition. Sensitivity analysis reveals that points on cheeks and on forehead carry little useful information. After discarding them, not only the computational efficiency increases, but also the generalization performance slightly improves. Finally, we have studied the significance of image scales. Experiments show that facial expression recognition is mainly a low frequency process, and a spatial resolution of 64 pixels × 64 pixels is probably enough.

scholarly journals Facial Expression Recognition Based on Texture Features

2021 ◽  
Vol 24 (2) ◽  
pp. 144-148
Author(s):  
Alaa Nabeel Haj Najeb ◽  
Nasser Nasser
Keyword(s):  
Feature Extraction ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Texture Feature ◽  
Texture Features ◽  
Recognition System ◽  
Expression Recognition ◽  
Emotional States ◽  
Social Communications ◽  
Matlab Programming

Facial expressions are a form of non-verbal communication, they appear as changes on the surface of the facial skin according to one's inner emotional states, aims, or social communications. Classification of these expressions is a normal process for humans, but it is a challenging task for machines.Lately, interest in facial expression recognition has grown, and many systems have been developed to classify expressions from facial images. Any expression recognition system is comprised of three steps. The first one is face acquisition, then feature extraction, and finally classification. The classification accuracy depends primarily on the feature extraction step.  Therefore, in this research we study many texture feature extraction descriptors and compare their results under the same preprocessing circumstances; moreover, we propose two improvements for one of these descriptors, which give better results than the original one. We validate the results on two commonly used databases for expression recognition using Matlab programming language, wishing all of that to be an interesting point for researchers in this field.

Computer Intelligent Nursing Care of Critically III Patients Based on Improved Patient Facial Expression Recognition Method

2014 ◽  
Vol 543-547 ◽  
pp. 2350-2353
Author(s):  
Xiao Yan Wan
Keyword(s):  
Facial Expression ◽  
Critically Ill ◽  
Nursing Care ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Support Vector ◽  
Appearance Model ◽  
Expression Recognition ◽  
Recognition Method ◽  
The Face

In order to extract the expression features of critically ill patients, and realize the computer intelligent nursing, an improved facial expression recognition method is proposed based on the of active appearance model, the support vector machine (SVM) for facial expression recognition is taken in research, and the face recognition model structure active appearance model is designed, and the attribute reduction algorithm of rough set affine transformation theory is introduced, and the invalid and redundant feature points are removed. The critically ill patient expressions are classified and recognized based on the support vector machine (SVM). The face image attitudes are adjusted, and the self-adaptive performance of facial expression recognition for the critical patient attitudes is improved. New method overcomes the effect of patient attitude to the recognition rate to a certain extent. The highest average recognition rate can be increased about 7%. The intelligent monitoring and nursing care of critically ill patients are realized with the computer vision effect. The nursing quality is enhanced, and it ensures the timely treatment of rescue.

Sign in / Sign up

Export Citation Format

Share Document