基于多层级信息融合网络的微表情识别方法

A Micro-expression Recognition Method Based on Multi-level Information Fusion Network

微表情是人类情感表达过程中细微且不自主的表情变化,实现准确和高效的微表情识别,对于心理疾病的早期诊断和治疗有重要意义.现有的微表情识别方法大多未考虑面部产生微表情时各个关键部位间的联系,难以在小样本图像空间上捕捉到微表情的细微变化,导致识别率不高.为此,提出一种基于多层级信息融合网络的微表情识别方法.该方法包括一个基于频率幅值的视频帧选取策略,能从微表情视频中筛选出包含高强度表情信息的图像帧、一个基于自注意力机制和图卷积网络的多层级信息提取网络以及一个引入图像全局信息的融合网络,能从不同层次捕获人脸微表情的细微变化,来提高对特定类别的辨识度.在公开数据集上的实验结果表明,该方法能有效提高微表情识别的准确率,与其他先进方法相比,具有更好的性能.

Micro-expressions are subtle and involuntary changes during emotional expression.Accurate and efficient 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-expression 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 expressions 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.