Spatiotemporal association and graph-attention guided micro-expression recognition network
As a typical non-spontaneous expression,micro-expressions can reflect the true intentions of human beings and have important applications in polygraphs and other fields.In order to solve the problem of weak global region corre-lation related to micro-expression generation,a micro-expression recognition network based on spatiotemporal regional correlation and graph attention guidance is proposed.The network consists of spatiotemporal association module and graph attention module.Spatiotemporal association module aggregates the spatiotemporal features of different nodes in the global region of the face by combining AU with spatiotemporal graph convolution,and uses Transformer encoder to further construct regional correlations among distant nodes of the face to strengthen the spatiotemporal association from a global perspective.The graph attention module builds the graph structure based on the facial local areas,and includes the calculation of self-attention,to obtain accurate local area association features.Finally,the global spatiotemporal and local correlation features are fused to recognize micro-expressions.Experiments were conducted on three mainstream mi-cro-expression datasets,CASME,CASME Ⅱ,and SAMM,and achieved excellent recognition results of 82.46%,86.59%,and 80.88%,respectively.The experimental results show that the proposed method has better performance than other methods.
NETL
NSTL
万方数据
