# Spatiotemporal Recurrent Convolutional Networks for Recognizing Spontaneous Micro-expressions

arXiv: https://arxiv.org/abs/1901.04656 · HTML: https://mlsanity.com/paper/1901.04656/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2019.

## Built on by (most cited first, 25 of 25)

- [Video-based Facial Micro-Expression Analysis: A Survey of Datasets, Features and Algorithms](https://mlsanity.com/paper/2201.12728/) (2022)
- [Deep Learning for Micro-expression Recognition: A Survey](https://mlsanity.com/paper/2107.02823/) (2021)
- [GPT-4V with Emotion: A Zero-shot Benchmark for Generalized Emotion Recognition](https://mlsanity.com/paper/2312.04293/) (2023)
- [Feature refinement: An expression-specific feature learning and fusion method for micro-expression recognition](https://mlsanity.com/paper/2101.04838/) (2021)
- [HTNet for micro-expression recognition](https://mlsanity.com/paper/2307.14637/) (2023)
- [Short and Long Range Relation Based Spatio-Temporal Transformer for Micro-Expression Recognition](https://mlsanity.com/paper/2112.05851/) (2021)
- [Feature Representation Learning with Adaptive Displacement Generation and Transformer Fusion for Micro-Expression Recognition](https://mlsanity.com/paper/2304.04420/) (2023)
- [PiSLTRc: Position-informed Sign Language Transformer with Content-aware Convolution](https://mlsanity.com/paper/2107.12600/) (2021)
- [Learning to Rank Onset-Occurring-Offset Representations for Micro-Expression Recognition](https://mlsanity.com/paper/2310.04664/) (2023)
- [How Do Deepfakes Move? Motion Magnification for Deepfake Source Detection](https://mlsanity.com/paper/2212.14033/) (2022)
- [MPFNet: A Multi-Prior Fusion Network with a Progressive Training Strategy for Micro-Expression Recognition](https://mlsanity.com/paper/2506.09735/) (2025)
- [Deep Insights of Learning based Micro Expression Recognition: A Perspective on Promises, Challenges and Research Needs](https://mlsanity.com/paper/2210.04935/) (2022)
- [Evaluating and Correcting Human Annotation Bias in Dynamic Micro-Expression Recognition](https://mlsanity.com/paper/2603.04766/) (2026)
- [Multi-scale multi-modal micro-expression recognition algorithm based on transformer](https://mlsanity.com/paper/2301.02969/) (2023)
- [Multimodal Latent Emotion Recognition from Micro-expression and Physiological Signals](https://mlsanity.com/paper/2308.12156/) (2023)
- [Temporal and Spatial Feature Fusion Framework for Dynamic Micro Expression Recognition](https://mlsanity.com/paper/2505.16372/) (2025)
- [Region attention and graph embedding network for occlusion objective class-based micro-expression recognition](https://mlsanity.com/paper/2107.05904/) (2021)
- [A Dynamic 3D Spontaneous Micro-expression Database: Establishment and Evaluation](https://mlsanity.com/paper/2108.00166/) (2021)
- [An Empirical Study of Super-resolution on Low-resolution Micro-expression Recognition](https://mlsanity.com/paper/2310.10022/) (2023)
- [MMME: A Spontaneous Multi-Modal Micro-Expression Dataset Enabling Visual-Physiological Fusion](https://mlsanity.com/paper/2506.09834/) (2025)
- [Inference-Time Gaze Refinement for Micro-Expression Recognition: Enhancing Event-Based Eye Tracking with Motion-Aware Post-Processing](https://mlsanity.com/paper/2506.12524/) (2025)
- [FAMNet: Integrating 2D and 3D Features for Micro-expression Recognition via Multi-task Learning and Hierarchical Attention](https://mlsanity.com/paper/2508.13483/) (2025)
- [FMANet: A Novel Dual-Phase Optical Flow Approach with Fusion Motion Attention Network for Robust Micro-expression Recognition](https://mlsanity.com/paper/2510.07810/) (2025)
- [Dual-View Optical Flow for 4D Micro-Expression Recognition - A Multi-Stream Fusion Attention Approach](https://mlsanity.com/paper/2603.26849/) (2026)
- [EPIR: An Efficient Patch Tokenization, Integration and Representation Framework for Micro-expression Recognition](https://mlsanity.com/paper/2604.08106/) (2026)

## Abstract (arXiv)

Recently, the recognition task of spontaneous facial micro-expressions has attracted much attention with its various real-world applications. Plenty of handcrafted or learned features have been employed for a variety of classifiers and achieved promising performances for recognizing micro-expressions. However, the micro-expression recognition is still challenging due to the subtle spatiotemporal changes of micro-expressions. To exploit the merits of deep learning, we propose a novel deep recurrent convolutional networks based micro-expression recognition approach, capturing the spatial-temporal deformations of micro-expression sequence. Specifically, the proposed deep model is constituted of several recurrent convolutional layers for extracting visual features and a classificatory layer for recognition. It is optimized by an end-to-end manner and obviates manual feature design. To handle sequential data, we exploit two types of extending the connectivity of convolutional networks across temporal domain, in which the spatiotemporal deformations are modeled in views of facial appearance and geometry separately. Besides, to overcome the shortcomings of limited and imbalanced training samples, temporal data augmentation strategies as well as a balanced loss are jointly used for our deep network. By performing the experiments on three spontaneous micro-expression datasets, we verify the effectiveness of our proposed micro-expression recognition approach compared to the state-of-the-art methods.