This　paper　aims　to　develop　a　facial　expression　recognition　algorithm　for　a　personal　digital　assistance　application.　Based　on　the　Kinect　RGB-D　images,　we　propose　a　multiway　extreme　learning　machine　(MW-ELM)　for　facial　expression　recognition,　which　reduces　the　computing　complexity　significantly　by　processing　the　RGB　and　Depth　channels　separately　at　the　input　layer.　Referring　to　our　earlier　work　on　semi-supervised　online　sequential　extreme　learning　machine　(SOS-ELM)　that　enhances　the　application　to　do　the　fast　and　incremental　learning　based　on　a　few　labeled　samples　together　with　some　un-labeled　samples　of　the　specific　user,　we　propose　to　do　the　parameter　training　with　semi-supervising　and　on-line　sequential　methods　for　the　higher　hidden　layer.　The　experiment　of　our　proposed　multiway　semi-supervised　online　sequential　extreme　learning　machine　(MW-SOS-ELM)　applying　in　the　facial　expression　recognition,　shows　that　our　proposed　approach　achieves　almost　the　same　recognition　accuracy　with　SOS-ELM,　but　reduces　recognition　time　significantly,　under　the　same　configuration　of　hidden　nodes.　Additionally,　the　experiments　show　that　our　semi-supervised　learning　scheme　reduces　the　requirement　of　labeled　data　sharply.