Fusion: Practice and Applications
  FPA
  2692-4048
  2770-0070
  
   10.54216/FPA
   https://www.americaspg.com/journals/show/390
  
 
 
  
   2018
  
  
   2018
  
 
 
  
   Micro-Expression Recognition using 3D - CNN
  
  
   Bharati Vidyapeeth's College of Engineering, INDIA
   
    Vishal
    Dubey
   
   Bharati Vidyapeeth's College of Engineering, INDIA
   
    Bhavya
    Takkar
   
   Bharati Vidyapeeth's College of Engineering, INDIA
   
    P. Singh
    Lamba
   
  
  
   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 12 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.
  
  
   2020
  
  
   2020
  
  
   5
   13
  
  
   10.54216/FPA.010101
   https://www.americaspg.com/articleinfo/3/show/390