Deep Insights of Learning based Micro Expression Recognition: A Perspective on Promises, Challenges and Research Needs
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 33 of its 103 references that are in the map. First posted on arXiv 10 Oct 2022.
Builds on
33 of 103 references.
References · 33
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
- 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
- 2018DARTS: Differentiable Architecture Searchcited by 1,798
- 2016SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizecited by 1,060
- 2017Dynamic Routing Between Capsulescited by 689
- 2019Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentationcited by 260
- 2016AVEC 2016 - Depression, Mood, and Emotion Recognition Workshop and Challengecited by 69
- 2019M3ER: Multiplicative Multimodal Emotion Recognition Using Facial, Textual, and Speech Cuescited by 56
- 2022Video-based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithmscited by 51
- 2020EmotiCon: Context-Aware Multimodal Emotion Recognition using Frege's Principlecited by 50
21 more
- 2018Learning-based Video Motion Magnificationcited by 43
- 2018OFF-ApexNet on Micro-expression Recognition Systemcited by 43
- 2021Deep Learning for Micro-expression Recognition: A Surveycited by 43
- 2019Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression Recognitioncited by 41
- 2015Towards Reading Hidden Emotions: A comparative Study of Spontaneous Micro-expression Spotting and Recognition Methodscited by 37
- 2020Revealing the Invisible with Model and Data Shrinking for Composite-database Micro-expression Recognitioncited by 31
- 2018Enriched Long-term Recurrent Convolutional Network for Facial Micro-Expression Recognitioncited by 27
- 2019Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-expressionscited by 25
- 2021Feature refinement: An expression-specific feature learning and fusion method for micro-expression recognitioncited by 25
- 2019LEARNet Dynamic Imaging Network for Micro Expression Recognitioncited by 22
- 2020MER-GCN: Micro Expression Recognition Based on Relation Modeling with Graph Convolutional Networkcited by 21
- 2019Spontaneous Facial Micro-Expression Recognition using 3D Spatiotemporal Convolutional Neural Networkscited by 18
- 2018A Survey of Automatic Facial Micro-expression Analysis: Databases, Methods and Challengescited by 16
- 2020An Overview of Facial Micro-Expression Analysis: Data, Methodology and Challengecited by 14
- 2018Micro-Attention for Micro-Expression recognitioncited by 13
- 2020Micro-expression spotting: A new benchmarkcited by 13
- 2018A Review on Facial Micro-Expressions Analysis: Datasets, Features and Metricscited by 9
- 2021AffectiveNet: Affective-Motion Feature Learningfor Micro Expression Recognitioncited by 8
- 2020ICE-GAN: Identity-aware and Capsule-Enhanced GAN with Graph-based Reasoning for Micro-Expression Recognition and Synthesiscited by 7
- 2020Non-Linearities Improve OrigiNet based on Active Imaging for Micro Expression Recognitioncited by 7
- 2019EXPERTNet Exigent Features Preservative Network for Facial Expression Recognitioncited by 1
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2 papers.