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.

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 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 bidirectional gated recurrent units within deep residual network

2020 ◽  
Vol 13 (4) ◽  
pp. 527-543
Author(s):  
Wenjuan Shen ◽  
Xiaoling Li
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Clinical Medicine ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Expression Recognition ◽  
Safe Driving ◽  
Content Type ◽  
Proposed Model

Purposerecent years, facial expression recognition has been widely used in human machine interaction, clinical medicine and safe driving. However, there is a limitation that conventional recurrent neural networks can only learn the time-series characteristics of expressions based on one-way propagation information.Design/methodology/approachTo solve such limitation, this paper proposes a novel model based on bidirectional gated recurrent unit networks (Bi-GRUs) with two-way propagations, and the theory of identity mapping residuals is adopted to effectively prevent the problem of gradient disappearance caused by the depth of the introduced network. Since the Inception-V3 network model for spatial feature extraction has too many parameters, it is prone to overfitting during training. This paper proposes a novel facial expression recognition model to add two reduction modules to reduce parameters, so as to obtain an Inception-W network with better generalization.FindingsFinally, the proposed model is pretrained to determine the best settings and selections. Then, the pretrained model is experimented on two facial expression data sets of CK+ and Oulu- CASIA, and the recognition performance and efficiency are compared with the existing methods. The highest recognition rate is 99.6%, which shows that the method has good recognition accuracy in a certain range.Originality/valueBy using the proposed model for the applications of facial expression, the high recognition accuracy and robust recognition results with lower time consumption will help to build more sophisticated applications in real world.

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 Nonverbal Communication through Facial Expression in Diverse Conditions

2021 ◽  
Author(s):  
Mahesh Goyani
Keyword(s):  
Facial Expression ◽  
Template Matching ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Training Sample ◽  
Expression Recognition ◽  
Feature Based ◽  
Multi Level ◽  
Space Recognition ◽  
Vision Technique

In this chapter, we investigated computer vision technique for facial expression recognition, which increase both - the recognition rate and computational efficiency. Local and global appearance-based features are combined in order to incorporate precise local texture and global shapes. We proposed Multi-Level Haar (MLH) feature based system, which is simple and fast in computation. The driving factors behind using the Haar were its two interesting properties - signal compression and energy preservation. To depict the importance of facial geometry, we first segmented the facial components like eyebrows, eye, and mouth, and then applied feature extraction on these facial components only. Experiments are conducted on three well known publicly available expression datasets CK, JAFFE, TFEID and in-house WESFED dataset. The performance is measured against various template matching and machine learning classifiers. We achieved highest recognition rate for proposed operator with Discriminant Analysis Classifier. We studied the performance of proposed approach in several scenarios like expression recognition from low resolution, recognition from small training sample space, recognition in the presence of noise and so forth.

Selective Facial Expression Recognition Using fastICA

2012 ◽  
Vol 433-440 ◽  
pp. 2755-2761 ◽  
Author(s):  
Xiao Hua Zhang ◽  
Zhi Fei Liu ◽  
Ya Jun Guo ◽  
Li Qiang Zhao
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Bp Neural Network ◽  
Facial Expression Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Facial Features ◽  
Expression Recognition ◽  
Average Recognition Rate ◽  
Neural Network Classifiers

This paper proposes a facial expression recognition approach based on the combination of fastICA method and neural network classifiers. First we get some special facial expression regions, including eyebrows, eyes and mouth, in which wavelet transform is done to reduce the dimension. Then the fastICA method is used to extract these three facial features. Finally, BP neural network classifier is adopted to recognize facial expression. Experimental on the JAFFE database results show that the method is effective for both dimension reduction and recognition performance in comparison with traditional PCA and ICA method. We have obtained recognition rates as high as 93.33% in categorizing the facial expressions neutral, anger, or sadness. The best average recognition rate achieves 90.48%.

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.

A Hybrid Approach for Facial Expression Recognition Using Extended Local Binary Patterns and Principal Component Analysis

2019 ◽  
Vol 8 (2) ◽  
pp. 1-25
Author(s):  
Gopal Krishan Prajapat ◽  
Rakesh Kumar
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Local Binary Pattern ◽  
Recognition Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Expression Recognition ◽  
Distance Method

Facial feature extraction and recognition plays a prominent role in human non-verbal interaction and it is one of the crucial factors among pose, speech, facial expression, behaviour and actions which are used in conveying information about the intentions and emotions of a human being. In this article an extended local binary pattern is used for the feature extraction process and a principal component analysis (PCA) is used for dimensionality reduction. The projections of the sample and model images are calculated and compared by Euclidean distance method. The combination of extended local binary pattern and PCA (ELBP+PCA) improves the accuracy of the recognition rate and also diminishes the evaluation complexity. The evaluation of proposed facial expression recognition approach will focus on the performance of the recognition rate. A series of tests are performed for the validation of algorithms and to compare the accuracy of the methods on the JAFFE, Extended Cohn-Kanade images database.

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