The micro-expression recognition (MER) has gained high attention in real-world applications: human-computer interaction, depression estimation, virtual reality, etc. However, MER systems still face difficulty in capturing the subtle information with spatial appearance of micro expressions. In this paper, an efficient and robust motion flow-guided MicroNet framework is proposed for MER. The proposed MicroNet consists: motion flow generator (MFGen) and avalanche feature (AFeat) block. The MFGen is introduced to extract the temporal changes at expressive regions by analyzing the motion intensity of pixels among the frames. Further, the AFeat block is designed to capture the spatiotemporal feature using a multi-lateral complementary feature (MCFeat) block. The MCFeat block is proposed to elicit the coarse and deep edge responses from multi-scale receptive fields. Thus, the proposed MicroNet estimates not only momentary variations but also learns affective appearance features of micro expressions. Furthermore, the efficacy of the proposed MicroNet is examined by conducting experiments on two experimental setups using six comprehensive datasets: CASME-I, CASME-II, CAS(ME)2, SAMM, SMIC and COMPOSITE, with two validation schemes: leave-one-subject-out and cross-domain to demonstrate the generalization capacity and robustness of the proposed framework. Also, eight ablation experiments have been executed to validate each module’s role in the MicroNet framework.

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 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 forge ahead in MER research. Finally, we point out the future directions, research needs and draw our conclusions.