Emotion　recognition　based　on　facial　expressions　has　low　accuracy　and　doubtful　reliability　because　of　the　exis-tence　of　fake　expressions.　In　this　paper,　a　method　is　proposed　to　recognize　fake　emotions　based　on　multivisual　information　generated　from　single　information　sources,　including　facial　expressions,　eye　states　and　physiological　signals　captured　from　video.　An　algorithm　based　on　a　graph　neural　network　is　used　to　extract　spatial　and　spectral　domain　features　from　facial　images　for　facial　expression　recognition.　A　model-based　method　is　used　for　decomposing　RGB　signals　into　heart　rates.　A　deep　model　trained　by　a　labeled　dataset　that　we　created　is　used　to　segment　the　eye　region.　After　obtaining　the　signals　extracted　from　video,　different　fusion　strategies　are　applied　to　evaluate　emotion　recognition　performance　based　on　multiple　signals.　In　the　experiment,　the　CK+,　TFEID,　JAFFE,　RAF,　PURE,　and　ESLD　datasets　are　used　to　measure　the　accuracy　of　facial　expression　recognition,　heart　rate　detection　and　eye　region　segmentation.　The　results　show　that　multimodality　is　effective　in　improving　accuracy　and　that　eye　state　can　be　considered　a　cue　for　trusted　emotion　recognition.　Compared　with　the　method　based　on　the　eye　state　and　noncontact　physiological　signal,　the　accuracy　of　multimodality　can　be　improved　by　26.19%　and　9.52%,　respectively.