基于双分支轻量化网络的微表情识别算法
Microexpression Recognition Algorithm Based on a Two-Branch Lightweight Network
张波 1武瑀繁1
作者信息
- 1. 沈阳化工大学计算机科学与技术学院,辽宁 沈阳 110142
- 折叠
摘要
针对采用卷积神经网络识别微表情时提高精度后往往会伴随复杂性增加的问题,提出一种改进的双流轻量级注意力网络(EDSMISEViTNet)用于微表情识别.首先对微表情样本进行预处理,提取峰值帧作为空间特征,采用TV-L1光流法提取起始帧和峰值帧之间的时间特征;然后基于MobileViT网络改进并设计了Inception和SE模块相结合的MI模块,并加入注意力模块以高效提取有效特征;将时间特征和空间特征分别输入该网络,对结果特征进行拼接融合继而分类.为使结果更加准确,将CASME II、SAMM以及SMIC数据集组合为复合数据集进行实验.实验结果表明,所提模型的训练参数量仅为3.9×106,处理单个样本的时间为71.8 ms.与现有方法相比,所提方法在保证低参数量的同时,准确率也具有良好表现.
Abstract
One of the common challenges encountered in microexpression recognition using convolutional neural networks is the heightened complexity caused by increased accuracy.To address this challenge,this study introduces an enhanced lightweight dual-stream attention network,called the enhanced dual-stream MISEViT network(EDSMISEViTNet),for microexpression recognition.First,microexpression samples are preprocessed,and peak frames are extracted as spatial features.Additionally,the TV-L1 optical flow method is used to extract the temporal features between the start frame and the vertex frame of each sample.Furthermore,this study improves the MobileViT network by designing an MI module that combines Inception and SE modules and introduces an attention module for efficient feature extraction.Temporal and spatial features are separately fed into this network,and the resultant features are concatenated,fused,and subsequently subjected to classification.To enhance precision,the CASME II,SAMM,and SMIC datasets are combined into a composite dataset for experimentation.The results reveal that the proposed algorithm model requires a training parameter count of only 3.9×106 and processes a single sample in just 71.8 ms.Compared with the existing methods,this approach achieves excellent accuracy while maintaining a low parameter count.
关键词
微表情识别/双流卷积神经网络/TV-L1光流法/视觉Transformer/注意力机制/MobileViT网络Key words
microexpression recognition/two-flow convolutional neural network/TV-L1 optical flow method/Visual Transformer/attention mechanism/MobileViT network引用本文复制引用
基金项目
辽宁省教育厅科学研究项目(LJ2020023)
出版年
2024
NETL
NSTL
万方数据