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

## TL;DR

This paper introduces a consolidated Eulerian framework that jointly amplifies and expands micro-expression signals, improving the accuracy and efficiency of facial micro-expression recognition over existing methods.

## Contribution

It proposes a unified approach that combines motion magnification and time interpolation into a single framework, capturing their underlying connections for better performance.

## Key findings

- Outperforms state-of-the-art in accuracy on public MER databases.
- Achieves faster processing of micro-expression clips.
- Enhances distinguishability of subtle facial movements.

## Abstract

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.