# A Boost in Revealing Subtle Facial Expressions: A Consolidated Eulerian Framework

arXiv: https://arxiv.org/abs/1901.07765 · HTML: https://mlsanity.com/paper/1901.07765/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2019.

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

- [Geometric Graph Representation with Learnable Graph Structure and Adaptive AU Constraint for Micro-Expression Recognition](https://mlsanity.com/paper/2205.00380/) (2022)
- [A comparative study on movement feature in different directions for micro-expression recognition](https://mlsanity.com/paper/2102.08068/) (2021)
- [Meta-Auxiliary Learning for Micro-Expression Recognition](https://mlsanity.com/paper/2404.12024/) (2024)

## Abstract (arXiv)

Facial Micro-expression Recognition (MER) distinguishes the underlying emotional states of spontaneous subtle facialexpressions. Automatic MER is challenging because that 1) the intensity of subtle facial muscle movement is extremely lowand 2) the duration of ME is transient.Recent works adopt motion magnification or time interpolation to resolve these issues. Nevertheless, existing works dividethem into two separate modules due to their non-linearity. Though such operation eases the difficulty in implementation, itignores their underlying connections and thus results in inevitable losses in both accuracy and speed. Instead, in this paper, weexplore their underlying joint formulations and propose a consolidated Eulerian framework to reveal the subtle facial movements.It expands the temporal duration and amplifies the muscle movements in micro-expressions simultaneously. Compared toexisting approaches, the proposed method can not only process ME clips more efficiently but also make subtle ME movementsmore distinguishable. Experiments on two public MER databases indicate that our model outperforms the state-of-the-art inboth speed and accuracy.