scholarly journals A Novel Based 3d Facial Expression Detection Using Recurrent Neural Network

2020 ◽  
pp. 48-53
Author(s):  
Jaswanth K S ◽  
D. Stalin David
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Facial Expressions ◽  
Social Relations ◽  
Recurrent Neural Network ◽  
Breakdown Point ◽  
Early Age ◽  
Facial Expression Classification ◽  
Expression Classification ◽  
Highlight Extraction

People periodically have diverse facial expressions and disposition changes in this way. Human facial expression acknowledgment plays a really energetic part in social relations. The acknowledgment of feelings has been an dynamic breakdown point from early age. The real-time location of facial expressions like appall, upbeat, pitiful, irate, anxious, astonish. The proposed framework can recognize 6 diverse facial expression. A facial expression acknowledgment framework needs to perform location and change to 3D image, then the facial highlight extraction, and facial expression classification is worn. Out proposed strategy we should be utilizing Recurrent Neural Network (RNN). This RNN show is prepared on JAFEE and Yale database dataset. This framework has capacity to screen individuals’ feelings, to segregate between feelings and name them fittingly.

Facial Expression Classification Based on Ensemble Convolutional Neural Network

2020 ◽  
Vol 57 (14) ◽  
pp. 141501
Author(s):  
周涛 Zhou Tao ◽  
吕晓琪 Lü Xiaoqi ◽  
任国印 Ren Guoyin ◽  
谷宇 Gu Yu ◽  
张明 Zhang Ming ◽  
...  
Keyword(s):  
Neural Network ◽  
Facial Expression ◽  
Convolutional Neural Network ◽  
Facial Expression Classification ◽  
Expression Classification

scholarly journals Classification of facial expressions using SVM and HOG

2020 ◽  
Vol 3 (2) ◽  
pp. 210-215
Author(s):  
Juliansyah Putra Tanjung ◽  
Muhathir Muhathir
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Information Acquisition ◽  
Support Vector ◽  
A Value ◽  
Average Accuracy ◽  
The Face ◽  
Facial Expression Classification ◽  
Expression Classification

The face is one of the human biometric which is often utilized as an important information of a person. One of the unique information of the face is facial expressions, expressions are information that is given indirectly about an expression of one's feelings. Because facial expressions have a unique pattern for each expression so that the pattern of facial expression will be tested with the computer by utilizing the Histogram of oriented gradient (HOG) descriptor as the extraction of existing features in each expression Face and information acquisition from HOG will be classified by utilizing the Support vector Mechine (SVM) method. The results of facial expression classification by utilizing the Extracaski HOG features reached 76.57% at a value of K = 500 with an average accuracy of 72.57%.

scholarly journals Analysis of Micro Facial Expression by Machine and Deep Learning Methods: Haar, CNN, and RNN

2021 ◽  
Vol 16 (1) ◽  
pp. 95-101
Author(s):  
Dibakar Raj Pant ◽  
Rolisha Sthapit
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Facial Expression ◽  
Facial Expressions ◽  
Recurrent Neural Network ◽  
Test Accuracy ◽  
Data Sets ◽  
Learning Methods ◽  
Facial Movements ◽  
Casme Ii

Facial expressions are due to the actions of the facial muscles located at different facial regions. These expressions are two types: Macro and Micro expressions. The second one is more important in computer vision. Analysis of micro expressions categorized by disgust, happiness, anger, sadness, surprise, contempt, and fear are challenging because of very fast and subtle facial movements. This article presents one machine learning method: Haar and two deep learning methods: Convolution Neural Network (CNN) and Recurrent Neural Network (RNN) to perform recognition of micro-facial expression analysis. First, Haar Cascade Classifier is used to detect the face as a pre-image-processing step. Secondly, those detected faces are passed through series of Convolutional Neural Network (CNN) layers for the features extraction. Thirdly, the Recurrent Neural Network (RNN) classifies micro facial expressions. Two types of data sets are used for training and testing of the proposed method: Chinese Academy of Sciences Micro-Expression II (CSAME II) and Spontaneous Actions and Micro-Movements (SAMM) database. The test accuracy of SAMM and CASME II are obtained as 84.76%, and 87% respectively. In addition, the distinction between micro facial expressions and non- micro facial expressions are analyzed by the ROC curve.

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