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.

A Facial Expression Recognition Algorithm Based on Computer Vision

2013 ◽  
Vol 380-384 ◽  
pp. 4057-4060
Author(s):  
Lang Guo ◽  
Jian Wang
Keyword(s):  
Computer Vision ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Three Dimensional ◽  
Recognition Algorithm ◽  
Expression Recognition ◽  
Two Dimensional ◽  
Manifold Structure ◽  
Facial Expression Database

Analyzing the defects of two-dimensional facial expression recognition algorithm, this paper proposes a new three-dimensional facial expression recognition algorithm. The algorithm is tested in JAFFE facial expression database. The results show that the proposed algorithm dynamically determines the size of the local neighborhood according to the manifold structure, effectively solves the problem of facial expression recognition, and has good recognition rate.

scholarly journals An expression recognition algorithm based on convolution neural network and RGB-D Images

2018 ◽  
Vol 173 ◽  
pp. 03066 ◽  
Author(s):  
HE binghua ◽  
CHEN zengzhao ◽  
LI gaoyang ◽  
JIANG lang ◽  
ZHANG zhao ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Reference Value ◽  
Recognition Algorithm ◽  
Future Research ◽  
Depth Information ◽  
Expression Recognition ◽  
Expression Library ◽  
Training Time

Aiming at the problem of recognition effect is not stable when 2D facial expression recognition in the complex illumination and posture changes. A facial expression recognition algorithm based on RGB-D dynamic sequence analysis is proposed. The algorithm uses LBP features which are robust to illumination, and adds depth information to study the facial expression recognition. The algorithm firstly extracts 3D texture features of preprocessed RGB-D facial expression sequence, and then uses the CNN to train the dataset. At the same time, in order to verify the performance of the algorithm, a comprehensive facial expression library including 2D image, video and 3D depth information is constructed with the help of Intel RealSense technology. The experimental results show that the proposed algorithm has some advantages over other RGB-D facial expression recognition algorithms in training time and recognition rate, and has certain reference value for future research in facial expression recognition.

scholarly journals A Robust Face Recognition Algorithm Based on an Improved Generative Confrontation Network

2021 ◽  
Vol 11 (24) ◽  
pp. 11588
Author(s):  
Huilin Ge ◽  
Yuewei Dai ◽  
Zhiyu Zhu ◽  
Biao Wang
Keyword(s):  
Face Recognition ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Expression Recognition ◽  
Generative Adversarial Network ◽  
Practical Applications ◽  
Adversarial Network ◽  
Partially Occluded

Objective: In practical applications, an image of a face is often partially occluded, which decreases the recognition rate and the robustness. Therefore, in response to this situation, an effective face recognition model based on an improved generative adversarial network (GAN) is proposed. Methods: First, we use a generator composed of an autoencoder and the adversarial learning of two discriminators (local discriminator and global discriminator) to fill and repair an occluded face image. On this basis, the Resnet-50 network is used to perform image restoration on the face. In our recognition framework, we introduce a classification loss function that can quantify the distance between classes. The image generated by the generator can only capture the rough shape of the missing facial components or generate the wrong pixels. To obtain a clearer and more realistic image, this paper uses two discriminators (local discriminator and global discriminator, as mentioned above). The images generated by the proposed method are coherent and minimally influence facial expression recognition. Through experiments, facial images with different occlusion conditions are compared before and after the facial expressions are filled, and the recognition rates of different algorithms are compared. Results: The images generated by the method in this paper are truly coherent and have little impact on facial expression recognition. When the occlusion area is less than 50%, the overall recognition rate of the model is above 80%, which is close to the recognition rate pertaining to the non-occluded images. Conclusions: The experimental results show that the method in this paper has a better restoration effect and higher recognition rate for face images of different occlusion types and regions. Furthermore, it can be used for face recognition in a daily occlusion environment, and achieve a better recognition effect.

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