# Apex-Centered Spatio-Temporal Rank Pooling and Gradient Attention for Micro-Expression Recognition

arXiv: https://arxiv.org/abs/2509.00056 · HTML: https://mlsanity.com/paper/2509.00056/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 51 references that are in the map.
- First posted on arXiv 25 Aug 2025.

## Builds on (most cited first, 14 of 14)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [OFF-ApexNet on Micro-expression Recognition System](https://mlsanity.com/paper/1805.08699/) (2018)
- [Towards Reading Hidden Emotions: A comparative Study of Spontaneous Micro-expression Spotting and Recognition Methods](https://mlsanity.com/paper/1511.00423/) (2015)
- [Less is More: Micro-expression Recognition from Video using Apex Frame](https://mlsanity.com/paper/1606.01721/) (2016)
- [MER-GCN: Micro Expression Recognition Based on Relation Modeling with Graph Convolutional Network](https://mlsanity.com/paper/2004.08915/) (2020)
- [Spontaneous Facial Micro-Expression Recognition using 3D Spatiotemporal Convolutional Neural Networks](https://mlsanity.com/paper/1904.01390/) (2019)
- [Feature Representation Learning with Adaptive Displacement Generation and Transformer Fusion for Micro-Expression Recognition](https://mlsanity.com/paper/2304.04420/) (2023)
- [Micro-Attention for Micro-Expression recognition](https://mlsanity.com/paper/1811.02360/) (2018)
- [MOL: Joint Estimation of Micro-Expression, Optical Flow, and Landmark via Transformer-Graph-Style Convolution](https://mlsanity.com/paper/2506.14511/) (2025)
- [AffectiveNet: Affective-Motion Feature Learningfor Micro Expression Recognition](https://mlsanity.com/paper/2104.07569/) (2021)
- [Non-Linearities Improve OrigiNet based on Active Imaging for Micro Expression Recognition](https://mlsanity.com/paper/2005.07991/) (2020)
- [Learning to Rank Onset-Occurring-Offset Representations for Micro-Expression Recognition](https://mlsanity.com/paper/2310.04664/) (2023)

## Built on by (most cited first, 1 of 1)

- [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)

## Abstract (arXiv)

Micro-expression recognition (MER) is a challenging task due to the subtle and fleeting nature of micro-expressions. Traditional input modalities, such as Apex Frame, Optical Flow, and Dynamic Image, often fail to adequately capture these brief facial movements, resulting in suboptimal performance. In this study, we introduce the Micro-expression Spatio-Temporal Image (MESTI), a micro-expression-specific reformulation of dynamic rank pooling that transforms a video sequence into a single image while emphasizing the onset-apex-offset temporal pattern of micro-expressions. Additionally, we present the Micro-expression Gradient Attention Network (MEGANet), which incorporates a proposed Gradient Attention block to enhance the extraction of fine-grained motion features from micro-expressions. By combining MESTI and MEGANet, we aim to establish a more effective approach to MER. Extensive experiments were conducted to evaluate the effectiveness of MESTI, comparing it with existing input modalities across regular architectures. Moreover, we demonstrate that replacing the input of previously published MER networks with MESTI leads to consistent performance improvements. The performance of MEGANet is also evaluated, showing that our proposed network achieves state-of-the-art results on the SMIC-HS, SAMM and competitive performance on CASMEII datasets, it also achieves leading performance in the reported cross-dataset evaluation settings. The combination of MESTI and MEGANet consistently outperforms the compared methods. These findings underscore the potential of MESTI as a superior input modality and MEGANet as an advanced recognition network, aiming to more effective MER systems in a variety of applications.