安徽工程大学学报2024，Vol.39Issue(5)：42-47,56,7.
融合光流与多注意力机制的微表情识别
Facial Micro-expressions Recognition Based on Fusion Optical Flow and Multi-attention Mechanism
摘要
Abstract
Aiming at the problem of low recognition accuracy due to the short duration and small motion range of micro-expressions occurring in specific areas of face,this paper explores a face micro-expression recognition method based on optical flow and multi-attention mechanism.Firstly,the facial optical flow features of vertex frames and initial frames are extracted by using Total Variation and L1 norm(TVL1)algorithm,and the optical flow strain is calculated as a supplementary feature;Then,three kinds of opti-cal flow features are used as inputs,and ResBlock,which introduces Global Attention Mechanism(GAM)and Dual Attention mechanism(DA),is applied to extract features and classify micro-expres-sions,so as to reduce facial information dispersion and enlarge global optical flow features.Finally,the classification experiments on CASME and CASME Ⅱ data sets are carried out to verify the feasibility and effectiveness of the proposed method for frontal face classification.关键词
融合光流/微表情识别/注意力机制/残差神经网络/深度学习Key words
fusion optical flow/micro-expressions recognition/attention mechanism/residual neural net-work/deep learning分类
信息技术与安全科学引用本文复制引用
唐绍语,魏利胜..融合光流与多注意力机制的微表情识别[J].安徽工程大学学报,2024,39(5):42-47,56,7.基金项目
安徽省教育厅自然科学研究重大项目(KJ2020ZD39) （KJ2020ZD39）
