  Facial micro-expressions are sudden involuntary minute muscle movements which
reveal true emotions that people try to conceal. Spotting a micro-expression
and recognizing it is a major challenge owing to its short duration and
intensity. Many works pursued traditional and deep learning based approaches to
solve this issue but compromised on learning low-level features and higher
accuracy due to unavailability of datasets. This motivated us to propose a
novel joint architecture of spatial and temporal network which extracts
time-contrasted features from the feature maps to contrast out micro-expression
from rapid muscle movements. The usage of time contrasted features greatly
improved the spotting of micro-expression from inconspicuous facial movements.
Also, we include a memory module to predict the class and intensity of the
micro-expression across the temporal frames of the micro-expression clip. Our
method achieves superior performance in comparison to other conventional
approaches on CASMEII dataset.
