Facial　expression　recognition　is　an　important　part　in　emotional　interaction　between　human　and　machine.　This　paper　proposes　a　facial　expression　recognition　approach　based　on　multi-classifier　fusion　with　stacking　algorithm.　The　kappa-error　diagram　is　employed　in　base-level　classifiers　selection,　which　gains　insights　about　which　individual　classifier　has　the　better　recognition　performance　and　how　diverse　among　them　to　help　improve　the　recognition　accuracy　rate　by　fusing　the　complementary　functions.　In　order　to　avoid　the　influence　of　the　chance　factor　caused　by　guessing　in　algorithm　evaluation　and　get　more　reliable　awareness　of　algorithm　performance,　kappa　and　informedness　besides　accuracy　are　utilized　as　measure　criteria　in　the　comparison　experiments.　To　verify　the　effectiveness　of　our　approach,　two　public　databases　are　used　in　the　experiments.　The　experiment　results　show　that　compared　with　individual　classifier　and　two　other　typical　ensemble　methods,　our　proposed　stacked　ensemble　system　does　recognize　facial　expression　more　accurately　with　less　standard　deviation.　It　overcomes　the　individual　classifier＇s　bias　and　achieves　more　reliable　recognition　results.