In　this　article,　an　expression　recognition　algorithm　based　on　feature　fusion　was　proposed.　First,　40　sets　of　Gabor　filters　were　selected　to　perform　filtering　operations　on　the　expression　images　to　enhance　the　texture　features　of　the　expression　images,　and　subsequently,　Local　Binary　Patterns(LBP)　operators　were　used　to　perform　feature　extraction　on　the　filtered　images　output　by　each　Gabor　channel　to　obtain　LBP　feature　maps.　Then　these　characteristic　graphs　are　taken　as　the　input　of　the　convolutional　neural　network　and　the　convolutional　neural　network　is　trained.Finally,　the　input　of　the　fully　connected　layer　of　the　trained　convolutional　neural　network　was　taken　out　separately　as　the　features　of　the　expression　image,　and　these　features　are　classified　and　identified　using　the　extreme　learning　machine　algorithm.　The　experimental　results　showed　that　the　method　in　this　paper　was　better　than　the　method　using　a　single　feature　and　can　effectively　improve　the　recognition　rate　in　expression　recognition.　©　2020　SPIE.