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Micro expression recognition (MER) is a very challenging area of research dueto its intrinsic nature and fine-grained changes. In the literature, theproblem of MER has been solved through handcrafted/descriptor-based techniques.However, in recent times, deep learning (DL) based techniques have been adoptedto gain higher performance for MER. Also, rich survey articles on MER areavailable by summarizing the datasets, experimental settings, conventional anddeep learning methods. In contrast, these studies lack the ability to conveythe impact of network design paradigms and experimental setting strategies forDL-based MER. Therefore, this paper aims to provide a deep insight into theDL-based MER frameworks with a perspective on promises in network modeldesigning, experimental strategies, challenges, and research needs. Also, thedetailed categorization of available MER frameworks is presented in variousaspects of model design and technical characteristics. Moreover, an empiricalanalysis of the experimental and validation protocols adopted by MER methods ispresented. The challenges mentioned earlier and network design strategies mayassist the affective computing research community in forging ahead in MERresearch. Finally, we point out the future directions, research needs, and drawour conclusions.

Deep Insights of Learning based Micro Expression Recognition: A Perspective on Promises, Challenges and Research Needs