Abstract:
Micro expression recognition (MER) is a very challenging area of research due to its intrinsic nature and finegrained changes. In the literature, the problem of MER has
been solved through handcrafted/descriptor-based techniques.
However, in recent times, deep learning (DL)-based techniques
have been adopted to gain higher performance for MER. Also,
rich survey articles on MER are available by summarizing the
data sets, experimental settings, conventional, and DL methods.
In contrast, these studies lack the ability to convey the impact
of network design paradigms and experimental setting strategies
for DL-based MER. Therefore, this article aims to provide a deep
insight into the DL-based MER frameworks with a perspective
on promises in network model designing, experimental strategies,
challenges, and research needs. Also, the detailed categorization
of available MER frameworks is presented in various aspects of
model design and technical characteristics. Moreover, an empirical analysis of the experimental and validation protocols adopted
by MER methods is presented. The challenges mentioned earlier
and network design strategies may assist the affective computing
research community in forge ahead in MER research. Finally,
we point out the future directions, research needs, and draw our
conclusions.
