scholarly journals A Facial Expression Recognition Model Based on Texture and Shape Features

2020 ◽  
Vol 37 (4) ◽  
pp. 627-632
Author(s):  
Aihua Li ◽  
Lei An ◽  
Zihui Che
Keyword(s):  
Feature Extraction ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Texture Features ◽  
Support Vector ◽  
Svm Classifier ◽  
Expression Recognition ◽  
Shape Features ◽  
The Face ◽  
Facial Expression Classification

With the development of computer vision, facial expression recognition has become a research hotspot. To further improve the accuracy of facial expression recognition, this paper probes deep into image segmentation, feature extraction, and facial expression classification. Firstly, the convolution neural network (CNN) was adopted to accurately separate the salient regions from the face image. Next, the Gaussian Markov random field (GMRF) model was improved to enhance the ability of texture features to represent image information, and a novel feature extraction algorithm called specific angle abundance entropy (SAAE) was designed to improve the representation ability of shape features. After that, the texture features were combined with shape features, and trained and classified by the support vector machine (SVM) classifier. Finally, the proposed method was compared with common methods of facial expression recognition on a standard facial expression database. The results show that our method can greatly improve the accuracy of facial expression recognition.

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.

scholarly journals Facial expression recognition using three-stage support vector machines

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Issam Dagher ◽  
Elio Dahdah ◽  
Morshed Al Shakik
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Support Vector ◽  
Expression Recognition ◽  
Second Stage ◽  
Vector Machines ◽  
Third Stage ◽  
The Face ◽  
Leave One Out ◽  
Gradient Feature

AbstractHerein, a three-stage support vector machine (SVM) for facial expression recognition is proposed. The first stage comprises 21 SVMs, which are all the binary combinations of seven expressions. If one expression is dominant, then the first stage will suffice; if two are dominant, then the second stage is used; and, if three are dominant, the third stage is used. These multilevel stages help reduce the possibility of experiencing an error as much as possible. Different image preprocessing stages are used to ensure that the features attained from the face detected have a meaningful and proper contribution to the classification stage. Facial expressions are created as a result of muscle movements on the face. These subtle movements are detected by the histogram-oriented gradient feature, because it is sensitive to the shapes of objects. The features attained are then used to train the three-stage SVM. Two different validation methods were used: the leave-one-out and K-fold tests. Experimental results on three databases (Japanese Female Facial Expression, Extended Cohn-Kanade Dataset, and Radboud Faces Database) show that the proposed system is competitive and has better performance compared with other works.

Subject Independent Facial Expression Recognition from 3D Face Models using Deformation Modeling

2011 ◽  
pp. 574-595
Author(s):  
Ruchir Srivastava ◽  
Shuicheng Yan ◽  
Terence Sim ◽  
Surendra Ranganath
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Test Subject ◽  
Future Research ◽  
Support Vector ◽  
Expression Recognition ◽  
Neutral Face ◽  
The Face ◽  
Future Research Directions ◽  
3D Information

Most of the works on Facial Expression Recognition (FER) have worked on 2D images or videos. However, researchers are now increasingly utilizing 3D information for FER. As a contribution, this chapter zooms in on 3D based approaches while introducing FER. Prominent works are reviewed briefly, and some of the issues involved in 3D FER are discussed along with the future research directions. In most of the FER approaches, there is a need for having a neutral (expressionless) face of the subject which might not always be practical. This chapter also presents a novel technique of feature extraction which does not require any neutral face of the test subject. A proposition has been verified experimentally that motion of a set of landmark points on the face, in exhibiting a particular facial expression, is similar in different persons. The presented approach shows promising results using Support Vector Machine (SVM) as the classifier.

scholarly journals Development of a Robust Multi-Scale Featured Local Binary Pattern for Improved Facial Expression Recognition

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5391
Author(s):  
Suraiya Yasmin ◽  
Refat Khan Pathan ◽  
Munmun Biswas ◽  
Mayeen Uddin Khandaker ◽  
Mohammad Rashed Iqbal Faruque
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Local Binary Pattern ◽  
Support Vector ◽  
Expression Recognition ◽  
Feature Extraction Method ◽  
Multi Scale ◽  
Essential Components ◽  
The Face ◽  
Facial Images

Compelling facial expression recognition (FER) processes have been utilized in very successful fields like computer vision, robotics, artificial intelligence, and dynamic texture recognition. However, the FER’s critical problem with traditional local binary pattern (LBP) is the loss of neighboring pixels related to different scales that can affect the texture of facial images. To overcome such limitations, this study describes a new extended LBP method to extract feature vectors from images, detecting each image from facial expressions. The proposed method is based on the bitwise AND operation of two rotational kernels applied on LBP(8,1) and LBP(8,2) and utilizes two accessible datasets. Firstly, the facial parts are detected and the essential components of a face are observed, such as eyes, nose, and lips. The portion of the face is then cropped to reduce the dimensions and an unsharp masking kernel is applied to sharpen the image. The filtered images then go through the feature extraction method and wait for the classification process. Four machine learning classifiers were used to verify the proposed method. This study shows that the proposed multi-scale featured local binary pattern (MSFLBP), together with Support Vector Machine (SVM), outperformed the recent LBP-based state-of-the-art approaches resulting in an accuracy of 99.12% for the Extended Cohn–Kanade (CK+) dataset and 89.08% for the Karolinska Directed Emotional Faces (KDEF) dataset.

scholarly journals A Texture Features based Robust Facial Expression Recognition

2022 ◽  
Vol 12 (1) ◽  
pp. 0-0
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Pattern Analysis ◽  
Texture Features ◽  
Support Vector ◽  
Expression Recognition ◽  
Local Descriptor ◽  
Extraction Pattern ◽  
Multiclass Support Vector Machine

Facial expression plays an important role in communicating emotions. In this paper, a robust method for recognizing facial expressions is proposed using the combination of appearance features. Traditionally, appearance features mainly divide any face image into regular matrices for the computation of facial expression recognition. However, in this paper, we have computed appearance features in specific regions by extracting facial components such as eyes, nose, mouth, and forehead, etc. The proposed approach mainly has five stages to detect facial expression viz. face detection and regions of interest extraction, feature extraction, pattern analysis using a local descriptor, the fusion of appearance features and finally classification using a Multiclass Support Vector Machine (MSVM). Results of the proposed method are compared with the earlier holistic representations for recognizing facial expressions, and it is found that the proposed method outperforms state-of-the-art methods.

scholarly journals Real-Time Facial Expression Recognition System for Video Big Sensor Data Security Application

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhi Yao ◽  
Hailing Sun ◽  
Guofu Zhou
Keyword(s):  
Facial Expression ◽  
Data Security ◽  
Facial Expression Recognition ◽  
Safety Management ◽  
Recognition System ◽  
Sensor Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Expression Recognition ◽  
Security Application

Facial video big sensor data (BSD) is the core data of wireless sensor network industry application and technology research. It plays an important role in many industries, such as urban safety management, unmanned driving, senseless attendance, and venue management. The construction of video big sensor data security application and intelligent algorithm model has become a hot and difficult topic in related fields based on facial expression recognition. This paper focused on the experimental analysis of Cohn–Kanade dataset plus (CK+) dataset with frontal pose and great clarity. Firstly, face alignment and the selection of peak image were utilized to preprocess the expression sequence. Then, the output vector from convolution network 1 and β-VAE were connected proportionally and input to support vector machine (SVM) classifier to complete facial expression recognition. The testing accuracy of the proposed model in CK + dataset can reach 99.615%. The number of expression sequences involved in training was 2417, and the number of expression sequences in testing was 519.

A Hierarchical Facial Feature Extraction Method for Expression Recognition

2012 ◽  
Vol 452-453 ◽  
pp. 802-806
Author(s):  
Jin Lin Han ◽  
Hong Zhang
Keyword(s):  
Feature Extraction ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Extraction Method ◽  
Facial Feature ◽  
Support Vector ◽  
Expression Recognition ◽  
Feature Extraction Method ◽  
Facial Identity ◽  
Facial Feature Extraction

With the development of computer visual technology, facial expression recognition plays an important role in the friendly and harmonious human-computer interaction field.Against the inadequacy of the original feature extraction method based on singular value decomposition, this paper proposed a hierarchical facial feature extraction method according to the needs of facial expression recognition, which combines the way of hierarchy and block to enhance the detail information of the image. Then utilize a combination of support vector machine to classify. The results of the two experiments show that the method is effective for the facial identity and expression recognition.

scholarly journals Facial Expression Recognition with LBP and ORB Features

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ben Niu ◽  
Zhenxing Gao ◽  
Bingbing Guo
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Real Life ◽  
Local Binary Patterns ◽  
Detection Algorithm ◽  
Support Vector ◽  
Expression Recognition ◽  
Region Division ◽  
The Face ◽  
Combined Features

Emotion plays an important role in communication. For human–computer interaction, facial expression recognition has become an indispensable part. Recently, deep neural networks (DNNs) are widely used in this field and they overcome the limitations of conventional approaches. However, application of DNNs is very limited due to excessive hardware specifications requirement. Considering low hardware specifications used in real-life conditions, to gain better results without DNNs, in this paper, we propose an algorithm with the combination of the oriented FAST and rotated BRIEF (ORB) features and Local Binary Patterns (LBP) features extracted from facial expression. First of all, every image is passed through face detection algorithm to extract more effective features. Second, in order to increase computational speed, the ORB and LBP features are extracted from the face region; specifically, region division is innovatively employed in the traditional ORB to avoid the concentration of the features. The features are invariant to scale and grayscale as well as rotation changes. Finally, the combined features are classified by Support Vector Machine (SVM). The proposed method is evaluated on several challenging databases such as Cohn-Kanade database (CK+), Japanese Female Facial Expressions database (JAFFE), and MMI database; experimental results of seven emotion state (neutral, joy, sadness, surprise, anger, fear, and disgust) show that the proposed framework is effective and accurate.

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