Emotion Recognition of Facial Expression Using Convolutional Neural Network

2020 ◽  
pp. 362-369
Author(s):  
Pradip Kumar ◽  
Ankit Kishore ◽  
Raksha Pandey
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Convolutional Neural Network ◽  
Emotion Recognition

scholarly journals Behaviour Based Data Dispatcher

2019 ◽  
Vol 8 (4) ◽  
pp. 12940-12944
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Facial Expression ◽  
Convolutional Neural Network ◽  
Emotion Recognition ◽  
Real Time ◽  
Human Life ◽  
The Other ◽  
Facial Emotion

Human life is a complex social structure. It is not possible for the humans to navigate without reading the other persons. They do it by identifying the faces. The state of response can be decided based on the mood of the opposite person. Whereas a person’s mood can be figured out by observing his emotion (Facial Gesture). The aim of the project is to construct a “Facial emotion Recognition” model using DCNN (Deep convolutional neural network) in real time. The model is constructed using DCNN as it is proven that DCNN work with greater accuracy than CNN (convolutional neural network). The facial expression of humans is very dynamic in nature it changes in split seconds whether it may be Happy, Sad, Angry, Fear, Surprise, Disgust and Neutral etc. This project is to predict the emotion of the person in real time. Our brains have neural networks which are responsible for all kinds of thinking (decision making, understanding). This model tries to develop these decisions making and classification skills by training the machine. It can classify and predict the multiple faces and different emotions at the very same time. In order to obtain higher accuracy, we take the models which are trained over thousands of datasets.

scholarly journals Micro-Facial Expression Recognition in Video Based on Optimal Convolutional Neural Network (MFEOCNN) Algorithm

2019 ◽  
Vol 9 (1) ◽  
pp. 2832-2841 ◽  
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Convolutional Neural Network ◽  
Emotion Recognition ◽  
Facial Expressions ◽  
Recognition Accuracy ◽  
Convolution Neural Network ◽  
Computational Time ◽  
Expression Recognition ◽  
Emotional States

Facial expression is a standout amongst the most imperative features of human emotion recognition. For demonstrating the emotional states facial expressions are utilized by the people. In any case, recognition of facial expressions has persisted a testing and intriguing issue with regards to PC vision. Recognizing the Micro-Facial expression in video sequence is the main objective of the proposed approach. For efficient recognition, the proposed method utilizes the optimal convolution neural network. Here the proposed method considering the input dataset is the CK+ dataset. At first, by means of Adaptive median filtering preprocessing is performed in the input image. From the preprocessed output, the extracted features are Geometric features, Histogram of Oriented Gradients features and Local binary pattern features. The novelty of the proposed method is, with the help of Modified Lion Optimization (MLO) algorithm, the optimal features are selected from the extracted features. In a shorter computational time it has the benefits of rapidly focalizing and effectively acknowledging with the aim of getting an overall arrangement or idea. Finally the recognition is done by Convolution Neural network (CNN). Then the performance of the proposed MFEOCNN method is analyzed in terms of false measures and recognition accuracy. This kind of emotion recognition is mainly used in medicine, marketing, E-learning, entertainment, law and monitoring. From the simulation, we know that the proposed approach achieves maximum recognition accuracy of 99.2% with minimum Mean Absolute Error (MAE) value. This results are compared with the existing for MicroFacial Expression Based Deep-Rooted Learning (MFEDRL), Convolutional Neural Network with Lion Optimization (CNN+LO) and Convolutional Neural Network (CNN) without optimization. The simulation of the proposed method is done in the working platform of MATLAB.

Graph-Embedded Convolutional Neural Network for Image-based EEG Emotion Recognition

2021 ◽  
pp. 1-1
Author(s):  
Tengfei Song ◽  
Wenming Zheng ◽  
Suyuan Liu ◽  
Yuan Zong ◽  
Zhen Cui ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Emotion Recognition
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