  Micro-expression, for its high objectivity in emotion detection, has emerged
to be a promising modality in affective computing. Recently, deep learning
methods have been successfully introduced into the micro-expression recognition
area. Whilst the higher recognition accuracy achieved, substantial challenges
in micro-expression recognition remain. The existence of micro expression in
small-local areas on face and limited size of available databases still
constrain the recognition accuracy on such emotional facial behavior. In this
work, to tackle such challenges, we propose a novel attention mechanism called
micro-attention cooperating with residual network. Micro-attention enables the
network to learn to focus on facial areas of interest covering different action
units. Moreover, coping with small datasets, the micro-attention is designed
without adding noticeable parameters while a simple yet efficient transfer
learning approach is together utilized to alleviate the overfitting risk. With
extensive experimental evaluations on three benchmarks (CASMEII, SAMM and SMIC)
and post-hoc feature visualizations, we demonstrate the effectiveness of the
proposed micro-attention and push the boundary of automatic recognition of
micro-expression.
