Facial micro-expression recognition based on local region of the key frame

2020 ◽  
Author(s):  
Wenjun Zhong ◽  
Xinhe Yu ◽  
Ling shi ◽  
Zhihua Xie
Keyword(s):  
Local Region ◽  
Expression Recognition ◽  
Key Frame ◽  
Micro Expression

Transfer learning for micro-expression recognition based on the difference key frame images

2021 ◽  
Author(s):  
Zhihua Xie ◽  
Le Wang ◽  
Ling Shi ◽  
Jiawei Fan ◽  
sijia Cheng
Keyword(s):  
Transfer Learning ◽  
Expression Recognition ◽  
Key Frame ◽  
The Difference ◽  
Micro Expression

Micro-Expression Recognition using 3D - CNN

2020 ◽  
pp. 5-13
Author(s):  
Vishal Dubey ◽  
◽  
◽  
◽  
Bhavya Takkar ◽  
...  
Keyword(s):  
Emotional Response ◽  
High Speed ◽  
Actual State ◽  
Time Interval ◽  
Short Time Interval ◽  
Expression Recognition ◽  
Specific Emotion ◽  
3D Cnn ◽  
Short Time ◽  
Micro Expression

Micro-expression comes under nonverbal communication, and for a matter of fact, it appears for minute fractions of a second. One cannot control micro-expression as it tells about our actual state emotionally, even if we try to hide or conceal our genuine emotions. As we know that micro-expressions are very rapid due to which it becomes challenging for any human being to detect it with bare eyes. This subtle-expression is spontaneous, and involuntary gives the emotional response. It happens when a person wants to conceal the specific emotion, but the brain is reacting appropriately to what that person is feeling then. Due to which the person displays their true feelings very briefly and later tries to make a false emotional response. Human emotions tend to last about 0.5 - 4.0 seconds, whereas micro-expression can last less than 1/2 of a second. On comparing micro-expression with regular facial expressions, it is found that for micro-expression, it is complicated to hide responses of a particular situation. Micro-expressions cannot be controlled because of the short time interval, but with a high-speed camera, we can capture one's expressions and replay them at a slow speed. Over the last ten years, researchers from all over the globe are researching automatic micro-expression recognition in the fields of computer science, security, psychology, and many more. The objective of this paper is to provide insight regarding micro-expression analysis using 3D CNN. A lot of datasets of micro-expression have been released in the last decade, we have performed this experiment on SMIC micro-expression dataset and compared the results after applying two different activation functions.

Micro-Expression Recognition Using 3D-CNN Layering

2020 ◽  
pp. 123-140
Author(s):  
Prerit Rathi ◽  
Rajat Sharma ◽  
Prateek Singal ◽  
Puneet Singh Lamba ◽  
Gopal Chaudhary ◽  
...  
Keyword(s):  
Expression Recognition ◽  
3D Cnn ◽  
Micro Expression
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