scholarly journals Facial Expression Recognition Using Kernel Entropy Component Analysis Network and DAGSVM

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiangmin Chen ◽  
Li Ke ◽  
Qiang Du ◽  
Jinghui Li ◽  
Xiaodi Ding
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Directed Acyclic Graph ◽  
Recognition Rate ◽  
Component Analysis ◽  
Support Vector ◽  
Expression Recognition ◽  
Final Output ◽  
High Level ◽  
Entropy Component

Facial expression recognition (FER) plays a significant part in artificial intelligence and computer vision. However, most of facial expression recognition methods have not obtained satisfactory results based on low-level features. The existed methods used in facial expression recognition encountered the major issues of linear inseparability, large computational burden, and data redundancy. To obtain satisfactory results, we propose an innovative deep learning (DL) model using the kernel entropy component analysis network (KECANet) and directed acyclic graph support vector machine (DAGSVM). We use the KECANet in the feature extraction stage. In the stage of output, binary hashing and blockwise histograms are adopted. We sent the final output features to the DAGSVM classifier for expression recognition. We test the performance of our proposed method on three databases of CK+, JAFFE, and CMU Multi-PIE. According to the experiment results, the proposed method can learn high-level features and provide more recognition information in the stage of training, obtaining a higher recognition rate.

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.

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.

FEATURE EXTRACTION THROUGH CROSS-PHASE CONGRUENCY FOR FACIAL EXPRESSION ANALYSIS

2009 ◽  
Vol 23 (03) ◽  
pp. 617-635 ◽  
Author(s):  
IOAN BUCIU ◽  
IOAN NAFORNITA
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition Task ◽  
Component Analysis ◽  
Support Vector ◽  
Expression Recognition ◽  
Human Face ◽  
Phase Congruency ◽  
Face Analysis ◽  
Linear Discriminant

Human face analysis has attracted a large number of researchers from various fields, such as computer vision, image processing, neurophysiology or psychology. One of the particular aspects of human face analysis is encompassed by facial expression recognition task. A novel method based on phase congruency for extracting the facial features used in the facial expression classification procedure is developed. Considering a set of image samples comprising humans expressing various expressions, this new approach computes the phase congruency map between the samples. The analysis is performed in the frequency space where the similarity (or dissimilarity) between sample phases is measured to form discriminant features. The experiments were run using samples from two facial expression databases. To assess the method's performance, the technique is compared to the state-of-the art techniques utilized for classifying facial expressions, such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA), and Gabor jets. The features extracted by the aforementioned techniques are further classified using two classifiers: a distance-based classifier and a Support Vector Machine-based classifier. Experiments reveal superior facial expression recognition performance for the proposed approach with respect to other techniques.

scholarly journals Facial Expression Recognition Based on Convolutional Neural Network Fusion SIFT Features of Mobile Virtual Reality

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Fuguang Yao ◽  
Liudong Qiu
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Convolutional Neural Network ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Support Vector ◽  
Data Sets ◽  
Expression Recognition ◽  
Neural Network Structure ◽  
Sift Features

Facial expression recognition computer technology can obtain the emotional information of the person through the expression of the person to judge the state and intention of the person. The article proposes a hybrid model that combines a convolutional neural network (CNN) and dense SIFT features. This model is used for facial expression recognition. First, the article builds a CNN model and learns the local features of the eyes, eyebrows, and mouth. Then, the article features are sent to the support vector machine (SVM) multiclassifier to obtain the posterior probabilities of various features. Finally, the output result of the model is decided and fused to obtain the final recognition result. The experimental results show that the improved convolutional neural network structure ER2013 and CK+ data sets’ facial expression recognition rate increases by 0.06% and 2.25%, respectively.

PERFORMANCE COMPARISONS OF FACIAL EXPRESSION RECOGNITION IN JAFFE DATABASE

2008 ◽  
Vol 22 (03) ◽  
pp. 445-459 ◽  
Author(s):  
FRANK Y. SHIH ◽  
CHAO-FA CHUANG ◽  
PATRICK S. P. WANG
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Research Area ◽  
Feature Representation ◽  
Behavioral Measure ◽  
Support Vector ◽  
Expression Recognition ◽  
Linear Discriminant ◽  
Leave One Out

Facial expression provides an important behavioral measure for studies of emotion, cognitive processes, and social interaction. Facial expression recognition has recently become a promising research area. Its applications include human-computer interfaces, human emotion analysis, and medical care and cure. In this paper, we investigate various feature representation and expression classification schemes to recognize seven different facial expressions, such as happy, neutral, angry, disgust, sad, fear and surprise, in the JAFFE database. Experimental results show that the method of combining 2D-LDA (Linear Discriminant Analysis) and SVM (Support Vector Machine) outperforms others. The recognition rate of this method is 95.71% by using leave-one-out strategy and 94.13% by using cross-validation strategy. It takes only 0.0357 second to process one image of size 256 × 256.

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

2021 ◽  
pp. 517-544
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 ◽  
Local Binary Patterns ◽  
Component Analysis ◽  
Expression Recognition

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.

FACIAL EXPRESSION RECOGNITION BASED ON LOCAL BINARY PATTERN FEATURES AND SUPPORT VECTOR MACHINE

2014 ◽  
Vol 28 (06) ◽  
pp. 1456012 ◽  
Author(s):  
NHAN THI CAO ◽  
AN HOA TON-THAT ◽  
HYUNG IL CHOI
Keyword(s):  
Support Vector Machine ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Communication Systems ◽  
Local Binary Pattern ◽  
Recognition Rate ◽  
Support Vector ◽  
Expression Recognition ◽  
Face Images ◽  
Intelligent Communication

Facial expression recognition has been researched much in recent years because of their applications in intelligent communication systems. Many methods have been developed based on extracting Local Binary Pattern (LBP) features associating different classifying techniques in order to get more and more better effects of facial expression recognition. In this work, we propose a novel method for recognizing facial expressions based on Local Binary Pattern features and Support Vector Machine with two effective improvements. First is the preprocessing step and second is the method of dividing face images into nonoverlap square regions for extracting LBP features. The method was experimented on three typical kinds of database: small (213 images), medium (2040 images) and large (5130 images). Experimental results show the effectiveness of our method for obtaining remarkably better recognition rate in comparison with other methods.

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