scholarly journals Facial expression recognition based on improved VGG convolutional neural network

2021 ◽  
Vol 2083 (3) ◽  
pp. 032030
Author(s):  
Cui Dong ◽  
Rongfu Wang ◽  
Yuanqin Hang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Expression Recognition ◽  
Original Network ◽  
Feature Information ◽  
Fully Connected ◽  
A Current

Abstract With the development of artificial intelligence, facial expression recognition based on deep learning has become a current research hotspot. The article analyzes and improves the VGG16 network. First, the three fully connected layers of the original network are changed to two convolutional layers and one fully connected layer, which reduces the complexity of the network; Then change the maximum pooling in the network to local-based adaptive pooling to help the network select feature information that is more conducive to facial expression recognition, so that the network can be used on the facial expression datasets RAF-DB and SFEW. The recognition rate increased by 4.7% and 7% respectively.

Efficient facial expression recognition based on convolutional neural network

2021 ◽  
Vol 25 (1) ◽  
pp. 139-154
Author(s):  
Yongxiang Cai ◽  
Jingwen Gao ◽  
Gen Zhang ◽  
Yuangang Liu
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Convolutional Neural Network ◽  
Facial Expression Recognition ◽  
Data Augmentation ◽  
Recognition Rate ◽  
Classification Performance ◽  
Driving Safety ◽  
Expression Recognition ◽  
Before And After

The goal of research in Facial Expression Recognition (FER) is to build a robust and strong recognizability model. In this paper, we propose a new scheme for FER systems based on convolutional neural network. Part of the regular convolution operation is replaced by depthwise separable convolution to reduce the number of parameters and the computational workload; the self-adaption joint loss function is adopted to improve the classification performance. In addition, we balance our train set through data augmentation, and we preprocess the input images through illumination processing, face detection, and other methods, effectively maximizing the expression recognition rate. Experiments to validate our methods are conducted based on the TensorFlow platform and Fer2013 dataset. We analyze the experimental results before and after train set balancing and network model modification, and we compare our results with those of other researchers. The results show that our method is effective at increasing the expression recognition rate under the same experiment conditions. We further conduct an experiment on our own expression dataset relevant to driving safety, and it yields similar results.

scholarly journals Three-Stream Convolutional Neural Network with Squeeze-and-Excitation Block for Near-Infrared Facial Expression Recognition

Electronics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 385 ◽  
Author(s):  
Ying Chen ◽  
Zhihao Zhang ◽  
Lei Zhong ◽  
Tong Chen ◽  
Juxiang Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Near Infrared ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
Recognition Rate ◽  
Three Dimensional ◽  
Expression Recognition ◽  
Facial Expression Database

Near-infrared (NIR) facial expression recognition is resistant to illumination change. In this paper, we propose a three-stream three-dimensional convolution neural network with a squeeze-and-excitation (SE) block for NIR facial expression recognition. We fed each stream with different local regions, namely the eyes, nose, and mouth. By using an SE block, the network automatically allocated weights to different local features to further improve recognition accuracy. The experimental results on the Oulu-CASIA NIR facial expression database showed that the proposed method has a higher recognition rate than some state-of-the-art algorithms.

scholarly journals Facial Expression Recognition Based on Random Forest and Convolutional Neural Network

Information ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 375 ◽  
Author(s):  
Yingying Wang ◽  
Yibin Li ◽  
Yong Song ◽  
Xuewen Rong
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Random Forest ◽  
Facial Expression ◽  
Convolutional Neural Network ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Great Success ◽  
Expression Recognition

As an important part of emotion research, facial expression recognition is a necessary requirement in human–machine interface. Generally, a face expression recognition system includes face detection, feature extraction, and feature classification. Although great success has been made by the traditional machine learning methods, most of them have complex computational problems and lack the ability to extract comprehensive and abstract features. Deep learning-based methods can realize a higher recognition rate for facial expressions, but a large number of training samples and tuning parameters are needed, and the hardware requirement is very high. For the above problems, this paper proposes a method combining features that extracted by the convolutional neural network (CNN) with the C4.5 classifier to recognize facial expressions, which not only can address the incompleteness of handcrafted features but also can avoid the high hardware configuration in the deep learning model. Considering some problems of overfitting and weak generalization ability of the single classifier, random forest is applied in this paper. Meanwhile, this paper makes some improvements for C4.5 classifier and the traditional random forest in the process of experiments. A large number of experiments have proved the effectiveness and feasibility of the proposed method.

scholarly journals Facial Expression Analysis Based on Fusion Multi-Layer Convolutional Layer Feature Neural Network

2020 ◽  
Author(s):  
Hao Meng ◽  
Fei Yuan ◽  
Tianhao Yan
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Expression Analysis ◽  
Recognition Rate ◽  
Expression Recognition ◽  
Feature Vectors ◽  
Facial Expression Analysis ◽  
Deep Feature ◽  
Feature Information ◽  
The Stability

Concerning the problem that the current facial expression analysis based on convolutional neural network (CNN) only uses the features of the last convolutional layer but the recognition rate is not high, this paper proposes the use of sub-deep convolutional layer features and builds a CNN model which fuses the features of multi-layer convolutional layers. The model uses a CNN for feature extraction and saves the deepest feature vectors and sub-deep feature vectors of the expression images. The sub-deep feature vector is used as the input of the multilayer CNN established in this paper. The processed fourth convolution layer feature is fused with the deepest feature previously saved to perform facial expression analysis. Experiments are performed on FERPLUS dataset, Cohn-Kanade dataset (CK+) and JAFFE dataset. The experimental results show that the improved network structure proposed in this paper can capture richer feature information during facial expression analysis, which greatly improves the accuracy of expression recognition and the stability of the network. Compared with the original CNN-based facial expression analysis using only the last layer of convolution layer features, using multi-layer fusion features on three kinds of datasets can improve the expression recognition rate by 33.3%, 2.3% and 22%, respectively.

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.

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%.

scholarly journals Facial Expression Identification using Regularized Supervised Distance Preserving Projection

2021 ◽  
Vol 69 (2) ◽  
pp. 70-75
Author(s):  
Sohana Jahan ◽  
Moriyam Akter ◽  
Sifta Yeasmin ◽  
Farhana Ahmed Simi
Keyword(s):  
Artificial Intelligence ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Rapid Development ◽  
Recognition Rate ◽  
Automatic Recognition ◽  
Expression Recognition ◽  
Key Technology ◽  
Crucial Step ◽  
State Of Art

Facial expression recognition is one of the most reliable and a key technology of advanced human-computer interaction with the rapid development of computer vision and artificial intelligence. Nowadays, there has been a growing interest in improving expression recognition techniques. In most of the cases, automatic recognition system’s efficiency depends on the represented facial expression feature. Even the best classifier may fail to achieve a good recognition rate if inadequate features are provided. Therefore, feature extraction is a crucial step of the facial expression recognition process. In this paper, we have used Regularized Supervised Distance Preserving Projection for extracting the best features of the images. Numerical experiment shows that the use of this technique outperforms many of state of art approaches in terms of recognition rate. Dhaka Univ. J. Sci. 69(2): 70-75, 2021 (July)

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.

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