# RMES: Real-Time Micro-Expression Spotting Using Phase From Riesz Pyramid

arXiv: https://arxiv.org/abs/2305.05523 · HTML: https://mlsanity.com/paper/2305.05523/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 9 May 2023.

## Builds on (most cited first, 8 of 8)

- [Towards Reading Hidden Emotions: A comparative Study of Spontaneous Micro-expression Spotting and Recognition Methods](https://mlsanity.com/paper/1511.00423/) (2015)
- [SAMM Long Videos: A Spontaneous Facial Micro- and Macro-Expressions Dataset](https://mlsanity.com/paper/1911.01519/) (2019)
- [MIMAMO Net: Integrating Micro- and Macro-motion for Video Emotion Recognition](https://mlsanity.com/paper/1911.09784/) (2019)
- [3D-CNN for Facial Micro- and Macro-expression Spotting on Long Video Sequences using Temporal Oriented Reference Frame](https://mlsanity.com/paper/2105.06340/) (2021)
- [Objective Classes for Micro-Facial Expression Recognition](https://mlsanity.com/paper/1708.07549/) (2017)
- [Shallow Optical Flow Three-Stream CNN for Macro- and Micro-Expression Spotting from Long Videos](https://mlsanity.com/paper/2106.06489/) (2021)
- [Spotting Micro-Expressions on Long Videos Sequences](https://mlsanity.com/paper/1812.10306/) (2018)
- [Mean Oriented Riesz Features for Micro Expression Classification](https://mlsanity.com/paper/2005.06198/) (2020)

## Built on by (most cited first, 3 of 3)

- [Survey of Design Paradigms for Social Robots](https://mlsanity.com/paper/2407.20556/) (2024)
- [End-to-End Facial Expression Detection in Long Videos](https://mlsanity.com/paper/2504.07660/) (2025)
- [GeoMag: Geometric-Aware Video Motion Magnification via State Space Model](https://mlsanity.com/paper/2605.29762/) (2026)

## Abstract (arXiv)

Micro-expressions (MEs) are involuntary and subtle facial expressions that are thought to reveal feelings people are trying to hide. ME spotting detects the temporal intervals containing MEs in videos. Detecting such quick and subtle motions from long videos is difficult. Recent works leverage detailed facial motion representations, such as the optical flow, and deep learning models, leading to high computational complexity. To reduce computational complexity and achieve real-time operation, we propose RMES, a real-time ME spotting framework. We represent motion using phase computed by Riesz Pyramid, and feed this motion representation into a three-stream shallow CNN, which predicts the likelihood of each frame belonging to an ME. In comparison to optical flow, phase provides more localized motion estimates, which are essential for ME spotting, resulting in higher performance. Using phase also reduces the required computation of the ME spotting pipeline by 77.8%. Despite its relative simplicity and low computational complexity, our framework achieves state-of-the-art performance on two public datasets: CAS(ME)2 and SAMM Long Videos.