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Micro-Expression Recognition has become challenging, as it is extremelydifficult to extract the subtle facial changes of micro-expressions. Recently,several approaches proposed several expression-shared features algorithms formicro-expression recognition. However, they do not reveal the specificdiscriminative characteristics, which lead to sub-optimal performance. Thispaper proposes a novel Feature Refinement ({FR}) with expression-specificfeature learning and fusion for micro-expression recognition. It aims to obtainsalient and discriminative features for specific expressions and also predictexpression by fusing the expression-specific features. FR consists of anexpression proposal module with attention mechanism and a classificationbranch. First, an inception module is designed based on optical flow to obtainexpression-shared features. Second, in order to extract salient anddiscriminative features for specific expression, expression-shared features arefed into an expression proposal module with attention factors and proposalloss. Last, in the classification branch, labels of categories are predicted bya fusion of the expression-specific features. Experiments on three publiclyavailable databases validate the effectiveness of FR under different protocol.Results on public benchmarks demonstrate that our FR provides salient anddiscriminative information for micro-expression recognition. The results alsoshow our FR achieves better or competitive performance with the existingstate-of-the-art methods on micro-expression recognition.

Feature refinement: An expression-specific feature learning and fusion method for micro-expression recognition