Facial　Action　Unit　(AU)　recognition　is　a　challenging　problem,　where　the　subtle　muscle　movement　brings　diverse　AU　representations.　Recently,　AU　relations　are　utilized　to　assist　AU　recognition　and　improve　the　understanding　of　AUs.　Nevertheless,　simply　adopting　the　regular　Bayesian　networks　or　the　relations　between　AUs　and　emotions　is　not　enough　for　modelling　complex　AU　relations.　To　provide　quantitative　measurement　of　AU　relations　using　the　knowledge　from　FACS,　we　propose　an　AU　relation　quantization　autoencoder.　Moreover,　to　cope　with　the　diversity　of　AUs　generated　from　the　individual　representation　differences　and　other　environmental　impacts,　we　propose　a　dual-channel　graph　convolutional　neural　network　(DGCN)　obtaining　both　inherent　and　random　AU　relations.　The　first　channel　is　FACS-based　relation　graph　convolution　channel　(FACS-GCN)　embedding　prior　knowledge　of　FACS,　and　it　adjusts　the　network　to　the　inherent　AU　dependent　relations.　The　second　channel　is　data-learning-based　relation　graph　convolution　channel　(DLR-GCN)　based　on　metric　learning,　and　it　provides　robustness　for　individual　differences　and　environmental　changes.　Comprehensive　experiments　have　been　conducted　on　three　public　datasets:　CK+,　RAF-AU　and　DISFA.　The　experimental　results　demonstrate　that　our　proposed　DGCN　can　extract　the　hidden　relations　well,　thereby　achieving　great　performance　in　AU　recognition.　(c)　2023　Published　by　Elsevier　B.V.