TSNN : Three‐Stream Combining 2D and 3D Convolutional Neural Network for Micro‐Expression Recognition

2020 ◽  
Vol 16 (1) ◽  
pp. 98-107
Author(s):  
Chao Wu ◽  
Fan Guo
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Expression Recognition ◽  
2D And 3D ◽  
Micro Expression

Micro-expression recognition based on 3D flow convolutional neural network

2018 ◽  
Vol 22 (4) ◽  
pp. 1331-1339 ◽  
Author(s):  
Jing Li ◽  
Yandan Wang ◽  
John See ◽  
Wenbin Liu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Expression Recognition ◽  
3D Flow ◽  
Micro Expression

scholarly journals Dual Temporal Scale Convolutional Neural Network for Micro-Expression Recognition

2017 ◽  
Vol 8 ◽  
Author(s):  
Min Peng ◽  
Chongyang Wang ◽  
Tong Chen ◽  
Guangyuan Liu ◽  
Xiaolan Fu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Temporal Scale ◽  
Expression Recognition ◽  
Micro Expression

scholarly journals Spatiotemporal Convolutional Neural Network with Convolutional Block Attention Module for Micro-Expression Recognition

Information ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 380
Author(s):  
Boyu Chen ◽  
Zhihao Zhang ◽  
Nian Liu ◽  
Yang Tan ◽  
Xinyu Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Three Dimensional ◽  
Expression Recognition ◽  
Learning Framework ◽  
The Face ◽  
3D Cnn ◽  
Visual Attention Mechanism ◽  
Casme Ii ◽  
Micro Expression

A micro-expression is defined as an uncontrollable muscular movement shown on the face of humans when one is trying to conceal or repress his true emotions. Many researchers have applied the deep learning framework to micro-expression recognition in recent years. However, few have introduced the human visual attention mechanism to micro-expression recognition. In this study, we propose a three-dimensional (3D) spatiotemporal convolutional neural network with the convolutional block attention module (CBAM) for micro-expression recognition. First image sequences were input to a medium-sized convolutional neural network (CNN) to extract visual features. Afterwards, it learned to allocate the feature weights in an adaptive manner with the help of a convolutional block attention module. The method was testified in spontaneous micro-expression databases (Chinese Academy of Sciences Micro-expression II (CASME II), Spontaneous Micro-expression Database (SMIC)). The experimental results show that the 3D CNN with convolutional block attention module outperformed other algorithms in micro-expression recognition.

scholarly journals Micro-expression recognition with small sample size by transferring long-term convolutional neural network

2018 ◽  
Vol 312 ◽  
pp. 251-262 ◽  
Author(s):  
Su-Jing Wang ◽  
Bing-Jun Li ◽  
Yong-Jin Liu ◽  
Wen-Jing Yan ◽  
Xinyu Ou ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sample Size ◽  
Small Sample Size ◽  
Small Sample ◽  
Expression Recognition ◽  
Micro Expression
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