Vision Based Facial Expression Recognition Using Eigenfaces and Multi-SVM Classifier

2020 ◽  
pp. 662-673
Author(s):  
Hla Myat Maw ◽  
Soe Myat Thu ◽  
Myat Thida Mon
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Svm Classifier ◽  
Expression Recognition

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.

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.

Facial Expression Recognition Based on RS-SVM

2014 ◽  
Vol 543-547 ◽  
pp. 2329-2332
Author(s):  
Dong Li
Keyword(s):  
Facial Expression ◽  
Set Theory ◽  
Rough Set ◽  
Facial Expression Recognition ◽  
Rough Set Theory ◽  
Rapid Development ◽  
Classification Performance ◽  
Svm Classifier ◽  
Expression Recognition

In Recent years, with the rapid development of facial expression recognition technology, processing and classification of facial expression recognition has become a hotspot in application studies of remote sensing. Rough set theory (RS) and SVM have unique advantages in information processing and classification. This paper applies RS-SVM to facial expression recognition, briefly introduce the concepts of RS and principle of SVM, attributes reduction in RS theory as preposing system to get rid of redundancy attributes. Meanwhile, the SVM classifier works as postposing system helps training and classifying the facial expression recognition. Experimental results indicate this model not only raise the operating speed, but also improve classification performance, providing a new effective way in facial expression recognition technology.

scholarly journals Facial Expression Recognition Algorithm Based on Fusion of Transformed Multilevel Features and Improved Weighted Voting SVM

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Hao Meng ◽  
Fei Yuan ◽  
Yue Wu ◽  
Tianhao Yan
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Feature Fusion ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Svm Classifier ◽  
Expression Recognition ◽  
Semantic Features ◽  
Weighted Voting ◽  
Single Feature

In allusion to the shortcomings of traditional facial expression recognition (FER) that only uses a single feature and the recognition rate is not high, a FER method based on fusion of transformed multilevel features and improved weighted voting SVM (FTMS) is proposed. The algorithm combines the transformed traditional shallow features and convolutional neural network (CNN) deep semantic features and uses an improved weighted voting method to make a comprehensive decision on the results of the four trained SVM classifiers to obtain the final recognition result. The shallow features include local Gabor features, LBP features, and joint geometric features designed in this study, which are composed of distance and deformation characteristics. The deep feature of CNN is the multilayer feature fusion of CNN proposed in this study. This study also proposes to use a better performance SVM classifier with CNN to replace Softmax since the poor distinction between facial expressions. Experiments on the FERPlus database show that the recognition rate of this method is 17.2% higher than that of the traditional CNN, which proves the effectiveness of the fusion of the multilayer convolutional layer features and SVM. FTMS-based facial expression recognition experiments are carried out on the JAFFE and CK+ datasets. Experimental results show that, compared with the single feature, the proposed algorithm has higher recognition rate and robustness and makes full use of the advantages and characteristics of different features.

scholarly journals Robust hybrid framework for automatic facial expression recognition

2018 ◽  
Vol 7 (2) ◽  
pp. 568
Author(s):  
Gunavathi H S ◽  
Siddappa M
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Local Binary Patterns ◽  
Recognition System ◽  
Research Field ◽  
Feature Representation ◽  
Svm Classifier ◽  
Expression Recognition ◽  
Hybrid Framework

Over the last few years, facial expression recognition is an active research field, which has an extensive range of applications in the area of social interaction, social intelligence, autism detection and Human-computer interaction. In this paper, a   robust hybrid framework is presented to recognize the facial expressions, which enhances the efficiency and speed of recognition system by extracting significant features of a face. In the proposed framework, feature representation and extraction are done by using Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG). Later, the dimensionalities of the obtained features are reduced using Compressive Sensing (CS) algorithm and classified using multiclass SVM classifier. We investigated the performance of the proposed hybrid framework on two public databases such as CK+ and JAFFE data sets. The investigational results show that the proposed hybrid framework is a promising framework for recognizing and identifying facial expressions with varying illuminations and poses in real time.

scholarly journals Face Expression Recognition using Convolution Neural Network (CNN) Models

2020 ◽  
Vol 11 (4) ◽  
pp. 1-11
Author(s):  
Nahla Nour ◽  
Mohammed Elhebir ◽  
Serestina Viriri
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition Accuracy ◽  
Svm Classifier ◽  
Expression Recognition ◽  
Face Expression ◽  
Result Show ◽  
Face Expression Recognition

This paper proposes the design of a Facial Expression Recognition (FER) system based on deep convolutional neural network by using three model. In this work, a simple solution for facial expression recognition that uses a combination of algorithms for face detection, feature extraction and classification is discussed. The proposed method uses CNN models with SVM classifier and evaluates them, these models are Alex-net model, VGG-16 model and Res-Net model. Experiments are carried out on the Extended Cohn-Kanada (CK+) datasets to determine the recognition accuracy for the proposed FER system. In this study the accuracy of AlexNet model compared with Vgg16 model and ResNet model. The result show that AlexNet model achieved the best accuracy (88.2%) compared to other models.

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