scholarly journals Sparse Representation Classifier Embedding Subspace Mapping and Support Vector for Facial Expression Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shaoqin Lu ◽  
Lei Xue ◽  
Xiaoqing Gu
Keyword(s):  
Facial Expression ◽  
Sparse Representation ◽  
Facial Expression Recognition ◽  
Structural Information ◽  
Classification Performance ◽  
Support Vector ◽  
Expression Recognition ◽  
Training Samples ◽  
Label Information ◽  
Sparse Representation Classifier

With the development of integration and innovation of Internet and industry, facial expression recognition (FER) technology is widely applied in wireless communication and mobile edge computing. The sparse representation-based classification is a hot topic in computer vision and pattern recognition. It is one type of commonly used image classification algorithms for FER in recent years. To improve the accuracy of FER system, this study proposed a sparse representation classifier embedding subspace mapping and support vector (SRC-SM-SV). Based on the traditional sparse representation model, SRC-SM-SV maps the training samples into a subspace and extracts rich and discriminative features by using the structural information and label information of the training samples. SRC-SM-SV integrates the support vector machine to enhance the classification performance of sparse representation coding. The solution of SRC-SM-SV uses an alternate iteration method, which makes the optimization process of the algorithm simple and efficient. Experiments on JAFFE and CK+ datasets prove the effectiveness of SRC-SM-SV in FER.

scholarly journals A Modified Sparse Representation Method for Facial Expression Recognition

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Wei Wang ◽  
LiHong Xu
Keyword(s):  
Facial Expression ◽  
Sparse Representation ◽  
Facial Expression Recognition ◽  
Matching Pursuit ◽  
Iteration Process ◽  
Orthogonal Matching Pursuit ◽  
Expression Recognition ◽  
Training Samples ◽  
Reduction Methods ◽  
Representation Method

In this paper, we carry on research on a facial expression recognition method, which is based on modified sparse representation recognition (MSRR) method. On the first stage, we use Haar-like+LPP to extract feature and reduce dimension. On the second stage, we adopt LC-K-SVD (Label Consistent K-SVD) method to train the dictionary, instead of adopting directly the dictionary from samples, and add block dictionary training into the training process. On the third stage, stOMP (stagewise orthogonal matching pursuit) method is used to speed up the convergence of OMP (orthogonal matching pursuit). Besides, a dynamic regularization factor is added to iteration process to suppress noises and enhance accuracy. We verify the proposed method from the aspect of training samples, dimension, feature extraction and dimension reduction methods and noises in self-built database and Japan’s JAFFE and CMU’s CK database. Further, we compare this sparse method with classic SVM and RVM and analyze the recognition effect and time efficiency. The result of simulation experiment has shown that the coefficient of MSRR method contains classifying information, which is capable of improving the computing speed and achieving a satisfying recognition result.

scholarly journals Iranian kinect face database (IKFDB): a color-depth based face database collected by kinect v.2 sensor

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Seyed Muhammad Hossein Mousavi ◽  
S. Younes Mirinezhad
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Depth Image ◽  
Sensor Technology ◽  
Support Vector ◽  
Expression Recognition ◽  
Face Database ◽  
Depth Data ◽  
Color Depth

AbstractThis study presents a new color-depth based face database gathered from different genders and age ranges from Iranian subjects. Using suitable databases, it is possible to validate and assess available methods in different research fields. This database has application in different fields such as face recognition, age estimation and Facial Expression Recognition and Facial Micro Expressions Recognition. Image databases based on their size and resolution are mostly large. Color images usually consist of three channels namely Red, Green and Blue. But in the last decade, another aspect of image type has emerged, named “depth image”. Depth images are used in calculating range and distance between objects and the sensor. Depending on the depth sensor technology, it is possible to acquire range data differently. Kinect sensor version 2 is capable of acquiring color and depth data simultaneously. Facial expression recognition is an important field in image processing, which has multiple uses from animation to psychology. Currently, there is a few numbers of color-depth (RGB-D) facial micro expressions recognition databases existing. With adding depth data to color data, the accuracy of final recognition will be increased. Due to the shortage of color-depth based facial expression databases and some weakness in available ones, a new and almost perfect RGB-D face database is presented in this paper, covering Middle-Eastern face type. In the validation section, the database will be compared with some famous benchmark face databases. For evaluation, Histogram Oriented Gradients features are extracted, and classification algorithms such as Support Vector Machine, Multi-Layer Neural Network and a deep learning method, called Convolutional Neural Network or are employed. The results are so promising.

scholarly journals Facial Expression Analysis by Machine Learning

2010 ◽  
pp. 239-258
Author(s):  
Siu-Yeung Cho ◽  
Teik-Toe Teoh ◽  
Yok-Yen Nguwi
Keyword(s):  
Feature Selection ◽  
Facial Expression ◽  
Real Time ◽  
Facial Expression Recognition ◽  
Classification Performance ◽  
Difficult Problem ◽  
Facial Features ◽  
Expression Recognition ◽  
Real Time Processing ◽  
Good Classification Performance

Facial expression recognition is a challenging task. A facial expression is formed by contracting or relaxing different facial muscles on human face that results in temporally deformed facial features like wide-open mouth, raising eyebrows or etc. The challenges of such system have to address with some issues. For instances, lighting condition is a very difficult problem to constraint and regulate. On the other hand, real-time processing is also a challenging problem since there are so many facial features to be extracted and processed and sometimes, conventional classifiers are not even effective in handling those features and produce good classification performance. This chapter discusses the issues on how the advanced feature selection techniques together with good classifiers can play a vital important role of real-time facial expression recognition. Several feature selection methods and classifiers are discussed and their evaluations for real-time facial expression recognition are presented in this chapter. The content of this chapter is a way to open-up a discussion about building a real-time system to read and respond to the emotions of people from facial expressions.

scholarly journals Facial Expression Recognition using SVM with CNN and Handcrafted Features

2019 ◽  
Vol 8 (4) ◽  
pp. 3570-3574
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition System ◽  
Vital Role ◽  
Working Environment ◽  
Support Vector ◽  
Expression Recognition ◽  
Shopping Malls ◽  
Benchmark Datasets ◽  
Mind Set

The facial expression recognition system is playing vital role in many organizations, institutes, shopping malls to know about their stakeholders’ need and mind set. It comes under the broad category of computer vision. Facial expression can easily explain the true intention of a person without any kind of conversation. The main objective of this work is to improve the performance of facial expression recognition in the benchmark datasets like CK+, JAFFE. In order to achieve the needed accuracy metrics, the convolution neural network was constructed to extract the facial expression features automatically and combined with the handcrafted features extracted using Histogram of Gradients (HoG) and Local Binary Pattern (LBP) methods. Linear Support Vector Machine (SVM) is built to predict the emotions using the combined features. The proposed method produces promising results as compared to the recent work in [1].This is mainly needed in the working environment, shopping malls and other public places to effectively understand the likeliness of the stakeholders at that moment.

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.

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