A Boost in Revealing Subtle Facial Expressions: A Consolidated Eulerian Framework
This work addresses the problem of automatic facial micro-expression recognition for applications like emotion analysis, but it is incremental as it builds on existing motion magnification and time interpolation methods.
The paper tackled the challenge of recognizing subtle facial micro-expressions by proposing a consolidated Eulerian framework that simultaneously expands temporal duration and amplifies muscle movements, outperforming state-of-the-art methods in both speed and accuracy on two public databases.
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.