Dual-modality spatiotemporal feature learning for spontaneous facial expression recognition in e-learning using hybrid deep neural network

2019 ◽  
Vol 36 (4) ◽  
pp. 743-755 ◽  
Author(s):  
Xiaoliang Zhu ◽  
Zijian Chen
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Deep Neural Network ◽  
Feature Learning ◽  
Expression Recognition ◽  
Dual Modality ◽  
Spatiotemporal Feature ◽  
E Learning

A Deep Neural Network-Driven Feature Learning Method for Multi-view Facial Expression Recognition

2016 ◽  
Vol 18 (12) ◽  
pp. 2528-2536 ◽  
Author(s):  
Tong Zhang ◽  
Wenming Zheng ◽  
Zhen Cui ◽  
Yuan Zong ◽  
Jingwei Yan ◽  
...  
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Deep Neural Network ◽  
Feature Learning ◽  
Expression Recognition ◽  
Learning Method

scholarly journals Efficient CNN Approach for Facial Expression Recognition

2021 ◽  
Vol 2129 (1) ◽  
pp. 012083
Author(s):  
Gheyath Mustafa Zebari ◽  
Dilovan Asaad Zebari ◽  
Diyar Qader Zeebaree ◽  
Habibollah Haron ◽  
Adnan Mohsin Abdulazeez ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Deep Neural Network ◽  
Learning Technology ◽  
Expression Recognition ◽  
Machine Learning Technique ◽  
Learning Technique

Abstract In the last decade, the Facial Expression Recognition field has been studied widely and become the base for many researchers, and still challenging in computer vision. Machine learning technique used in facial expression recognition facing many problems, since human emotions expressed differently from one to another. Nevertheless, Deep learning that represents a novel area of research within machine learning technology has the ability for classifying people’s faces into different emotion classes by using a Deep Neural Network (DNN). The Convolution Neural Network (CNN) method has been used widely and proved as very efficient in the facial expression recognition field. In this study, a CNN technique for facial expression recognition has been presented. The performance of this study has been evaluated using the fer2013 dataset, the total number of images has been used. The accuracy of each epoch has been tested which is trained on 29068 samples, validate on 3589 samples. The overall accuracy of 69.85% has been obtained for the proposed method.

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