  Micro expression recognition (MER) is a very challenging area of research due
to its intrinsic nature and fine-grained 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 datasets, experimental settings, conventional and
deep learning 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 paper 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 forging ahead in MER
research. Finally, we point out the future directions, research needs, and draw
our conclusions.
