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

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.