A Survey on Factors Affecting Facial Expression Recognition based on Convolutional Neural Networks

2020 ◽  
Author(s):  
Emmanuel Dufourq
Keyword(s):  
Neural Networks ◽  
Facial Expression ◽  
Convolutional Neural Networks ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Factors Affecting

Convolutional Neural Networks for Facial Expression Recognition with Few Training Samples

2018 ◽  
Author(s):  
Zhongzhao Xie ◽  
Yongbo Li ◽  
Xinmei Wang ◽  
Wendi Cai ◽  
Jing Rao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Facial Expression ◽  
Convolutional Neural Networks ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Training Samples

Video-based facial expression recognition using multimodal deep convolutional neural networks

2019 ◽  
Vol 27 (4) ◽  
pp. 963-970
Author(s):  
潘仙张 PAN Xian-zhang ◽  
张石清 ZHANG Shi-qing ◽  
郭文平 GUO Wen-ping
Keyword(s):  
Neural Networks ◽  
Facial Expression ◽  
Convolutional Neural Networks ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Deep Convolutional Neural Networks

scholarly journals Feature Acquisition and Analysis for Facial Expression Recognition Using Convolutional Neural Networks

2017 ◽  
Vol 32 (5) ◽  
pp. F-H34_1-8 ◽  
Author(s):  
Taiki Nishime ◽  
Satoshi Endo ◽  
Naruaki Toma ◽  
Koji Yamada ◽  
Yuhei Akamine
Keyword(s):  
Neural Networks ◽  
Facial Expression ◽  
Convolutional Neural Networks ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Feature Acquisition

scholarly journals Facial Expression Analysis using Convolutional Neural Networks

2020 ◽  
Vol 9 (4) ◽  
pp. 1142-1146
Keyword(s):  
Neural Networks ◽  
Facial Expression ◽  
Real Time ◽  
Facial Expressions ◽  
Convolutional Neural Networks ◽  
Expression Analysis ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Real Time System ◽  
Facial Expression Analysis

Human feelings are mental conditions of sentiments that emerge immediately as opposed to cognitive exertion. Some of the basic feelings are happy, angry, neutral, sad and surprise. These internal feelings of a person are reflected on the face as Facial Expressions. This paper presents a novel methodology for Facial Expression Analysis which will aid to develop a facial expression recognition system. This system can be used in real time to classify five basic emotions. The recognition of facial expressions is important because of its applications in many domains such as artificial intelligence, security and robotics. Many different approaches can be used to overcome the problems of Facial Expression Recognition (FER) but the best suited technique for automated FER is Convolutional Neural Networks(CNN). Thus, a novel CNN architecture is proposed and a combination of multiple datasets such as FER2013, FER+, JAFFE and CK+ is used for training and testing. This helps to improve the accuracy and develop a robust real time system. The proposed methodology confers quite good results and the obtained accuracy may give encouragement and offer support to researchers to build better models for Automated Facial Expression Recognition systems.

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