  Micro-Expression Recognition has become challenging, as it is extremely
difficult to extract the subtle facial changes of micro-expressions. Recently,
several approaches proposed several expression-shared features algorithms for
micro-expression recognition. However, they do not reveal the specific
discriminative characteristics, which lead to sub-optimal performance. This
paper proposes a novel Feature Refinement ({FR}) with expression-specific
feature learning and fusion for micro-expression recognition. It aims to obtain
salient and discriminative features for specific expressions and also predict
expression by fusing the expression-specific features. FR consists of an
expression proposal module with attention mechanism and a classification
branch. First, an inception module is designed based on optical flow to obtain
expression-shared features. Second, in order to extract salient and
discriminative features for specific expression, expression-shared features are
fed into an expression proposal module with attention factors and proposal
loss. Last, in the classification branch, labels of categories are predicted by
a fusion of the expression-specific features. Experiments on three publicly
available databases validate the effectiveness of FR under different protocol.
Results on public benchmarks demonstrate that our FR provides salient and
discriminative information for micro-expression recognition. The results also
show our FR achieves better or competitive performance with the existing
state-of-the-art methods on micro-expression recognition.
