In　the　paper,　we　propose　a　novel　Data-aware　relation　graph　convolutional　neural　network　(DAR-GCN)　for　AU　recognition.　With　learning　and　updating　the　relation　dynamically,　it　facilitates　modeling　the　potential　dynamic　individual　facial　expressing　manner　and　accordingly　improves　the　AU　recognition　under　the　unconstrained　environment.　Taking　the　psychological　research　knowledge　of　AUs　as　a　reference,　we　adopt　the　consensus　widely-used　AUs　and　six　basic　emotions　as　vertexes,　and　their　co-occurrence　or　ex-occurrence　relations　between　AUs　and　the　emotion　dependent　relation　as　the　edges　to　construct　the　graph.　Moreover,　the　Data-aware　relation　Graph　Generator　(DAR-GG)　module　is　proposed　to　learn　the　relations　with　data-driven　metric　learning.　This　proposed　scheme　is　benefit　for　calculating　and　updating　AU　relations　from　data,　which　facilitates　to　extract　specific　relations　causing　by　individual　expressing　characteristics　as　well　as　inherent　relations　due　to　facial　anatomical　structure.　Comparative　experiments　are　done　on　three　public　datasets:　CK+,　RAF-AU　and　DISFA.　Experimental　results　demonstrate　that　our　proposed　method　achieves　a　higher　AU　recognition　accuracy　rate　than　the　baseline　based　on　the　graph　with　fixed　AU　relations　defined　from　the　psychological　knowledge.　Additionally,　our　proposed　approach　outperforms　several　existing　state-of-the-art　AU　recognition　method　by　utilizing　GCN-based　dynamic　AU　relations　learning　strategies.　(C)　2022　Elsevier　B.V.　All　rights　reserved.