In　recent　times,　due　to　the　fast　progression　of　artificial　intelligence　technology,　the　recognition　of　emotions　has　emerged　as　a　trending　area　of　study.Among　many　emotion　recognition　technologies,　the　method　based　on　expression　recognition　is　particularly　critical.　We　propose　a　two-stream　model　that　can　simultaneously　extract　static　and　dynamic　expression　features.　To　verify　the　information　representation　ability　of　the　two-stream　model　for　expression　images,　we　conducted　emotion　classification　experiments　based　on　a　deep　convolutional　neural　network　model　on　two　public　dynamic　expression　databases　(CK+　and　Oulu-CASIA).　Experimental　results　show　that　our　two-stream　model　has　significant　advantages　in　classification　performance　compared　to　single　static　or　dynamic　expression　classification　methods,　as　well　as　classification　techniques　based　on　3D　convolutional　neural　networks.　©　2024　SPIE.