# AffectiveNet: Affective-Motion Feature Learningfor Micro Expression Recognition

arXiv: https://arxiv.org/abs/2104.07569 · HTML: https://mlsanity.com/paper/2104.07569/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2021.

## Built on by (most cited first, 8 of 8)

- [Deep Learning for Micro-expression Recognition: A Survey](https://mlsanity.com/paper/2107.02823/) (2021)
- [Feature Representation Learning with Adaptive Displacement Generation and Transformer Fusion for Micro-Expression Recognition](https://mlsanity.com/paper/2304.04420/) (2023)
- [Deep Insights of Learning based Micro Expression Recognition: A Perspective on Promises, Challenges and Research Needs](https://mlsanity.com/paper/2210.04935/) (2022)
- [Efficient Neural Architecture Search for Emotion Recognition](https://mlsanity.com/paper/2303.13653/) (2023)
- [Multi-scale multi-modal micro-expression recognition algorithm based on transformer](https://mlsanity.com/paper/2301.02969/) (2023)
- [Apex-Centered Spatio-Temporal Rank Pooling and Gradient Attention for Micro-Expression Recognition](https://mlsanity.com/paper/2509.00056/) (2025)
- [Adaptive Fusion Network with Temporal-Ranked and Motion-Intensity Dynamic Images for Micro-expression Recognition](https://mlsanity.com/paper/2510.09730/) (2025)
- [DIANet: A Phase-Aware Dual-Stream Network for Micro-Expression Recognition via Dynamic Images](https://mlsanity.com/paper/2510.12219/) (2025)

## Abstract (arXiv)

Micro-expressions are hard to spot due to fleeting and involuntary moments of facial muscles. Interpretation of micro emotions from video clips is a challenging task. In this paper we propose an affective-motion imaging that cumulates rapid and short-lived variational information of micro expressions into a single response. Moreover, we have proposed an AffectiveNet:affective-motion feature learning network that can perceive subtle changes and learns the most discriminative dynamic features to describe the emotion classes. The AffectiveNet holds two blocks: MICRoFeat and MFL block. MICRoFeat block conserves the scale-invariant features, which allows network to capture both coarse and tiny edge variations. While MFL block learns micro-level dynamic variations from two different intermediate convolutional layers. Effectiveness of the proposed network is tested over four datasets by using two experimental setups: person independent (PI) and cross dataset (CD) validation. The experimental results of the proposed network outperforms the state-of-the-art approaches with significant margin for MER approaches.