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Feature refinement: An expression-specific feature learning and fusion method for micro-expression recognition
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DOI：10.1016/j.patcog.2021.108275.png)
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摘要
En 中文
Micro-expression recognition has become challenging, as it is extremely difficult to extract the subtle fa-cial changes of micro-expressions. Recently, several approaches have proposed various expression-shared features algorithms for micro-expression recognition. However, these approaches do not reveal the spe-cific discriminative characteristics, which leads to sub-optimal performance. This paper proposes a novel Feature Refinement (FeatRef) with expression-specific feature learning and fusion for micro-expression recognition that aims to obtain salient and discriminative features for specific expressions and predicts expressions by fusing expression-specific features. FeatRef consists of an expression proposal module with an attention mechanism and a classification branch. First, an inception module is designed based on op-tical flow to obtain expression-shared features. Second, to extract salient and discriminative features for specific expressions, expression-shared features are fed into an expression proposal module with atten-tion factors and proposal loss. Last, in the classification branch, category labels are predicted via a fusion of expression-specific features. Experiments on three publicly available databases validate the effective-ness of FeatRef under different protocols. The results on public benchmarks demonstrate that FeatRef provides salient and discriminative information for micro-expression recognition. The results also show that FeatRef achieves better or competitive performance with existing state-of-the-art methods on micro-expression recognition. (c) 2021 Elsevier Ltd. All rights reserved.
Keyword:
Micro-expression recognition
Deep learning
Attention mechanism
Shared feature
Feature refinement
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期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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