  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.
