A Micro-expression Recognition Method Based on Multi-level Information Fusion Network
Micro-expressions are subtle and involuntary changes during emotional expression.Accurate and effi-cient recognition of these is crucial for the early diagnosis and treatment of mental illnesses.Most of the existing methods often neglect the connections between key facial areas in micro-expressions,making it difficult to capture the subtle changes in small sample image spaces,resulting in low recognition rates.To address this,a micro-expres-sion recognition method is proposed based on a multi-level information fusion network.This method includes a video frame selection strategy based on frequency amplitude,which can select frames with high-intensity expres-sions from micro-expression videos.Additionally,this method includes a multi-level information extraction network using self-attention mechanisms and graph convolutional networks,and a fusion network that incorporates global image information,which can capture the subtle changes of facial micro-expressions from different levels to improve the recognition of specific categories.Experiments on public datasets show that our method effectively improves the accuracy and outperforms other advanced methods.
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