A　major　challenge　in　pattern　recognition　is　labeling　of　large　numbers　of　sam-　ples.　This　problem　has　been　solved　by　extending　supervised　learning　to　semi-supervised　learning.　Thus　semi-supervised　learning　has　become　one　of　the　most　important　methods　on　the　research　of　facial　expression　recognition.　Frontal　and　un-occluded　face　images　have　been　well　recognized　using　traditional　facial　expression　recognition　based　on　semi-　supervised　learning.　However,　pose-variants　caused　by　body　movement,　may　decrease　facial　expression　recognition　rate.　A　novel　facial　expression　recognition　algorithm　based　on　semi-supervised　learning　is　proposed　to　improve　the　robustness　in　multi-pose　facial　expression　recognition.　In　the　proposed　method,　transfer　learning　has　been　brought　into　semi-supervised　learning　to　solve　the　problem　of　multi-pose　facial　expression　recognition.　Experiments　show　that　our　method　is　competent　for　semi-supervised　facial　expression　recognition　on　the　condition　of　multi-pose.　The　recognition　rates　are　82.68%　and　87.71%　on　the　RaFD　database　and　BHU　database,　respectively.　©　2013.