论文标题
部分可观测时空混沌系统的无模型预测
Deep Insights of Learning based Micro Expression Recognition: A Perspective on Promises, Challenges and Research Needs
论文作者
论文摘要
由于其内在性质和细粒度的变化，微型表达识别（MER）是一个非常具有挑战性的研究领域。在文献中，MER的问题已通过基于手工/描述符的技术解决。但是，最近，已经采用了基于深度学习的技术（DL）技术来获得更高的MER性能。此外，可以通过总结数据集，实验设置，常规和深度学习方法来获得有关MER的丰富调查文章。相比之下，这些研究缺乏传达网络设计范例和对基于DL的MER的实验设置策略的影响的能力。因此，本文旨在深入了解基于DL的MER框架，以了解网络模型设计，实验策略，挑战和研究需求的承诺。此外，可用MER框架的详细分类以模型设计和技术特征的各个方面介绍。此外，提出了对MER方法采用的实验和验证方案的经验分析。前面提到的挑战和网络设计策略可能有助于情感计算研究界在MER研究中锻造。最后，我们指出未来的方向，研究需求并得出结论。
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.
