Low-resolution　infrared-based　human　activity　recognition　(HAR)　attracted　enormous　interests　due　to　its　low　cost　and　private.　In　this　article,　a　novel　semi-supervised　cross-domain　neural　network　(SCDNN)　based　on　8×8　low-resolution　infrared　sensor　is　proposed　for　accurately　identifying　human　activity　despite　changes　in　the　environment　at　a　low　cost.　The　SCDNN　consists　of　feature　extractor,　domain　discriminator,　and　label　classifier.　In　the　feature　extractor,　the　unlabeled　and　minimal　labeled　target　domain　data　are　trained　for　domain　adaptation　to　achieve　a　mapping　of　the　source　domain　and　target　domain　data.　The　domain　discriminator　employs　the　unsupervised　learning　to　migrate　data　from　the　source　domain　to　the　target　domain.　The　label　classifier　obtained　from　training　the　source　domain　data　improves　the　recognition　of　target　domain　activities　due　to　the　semi-supervised　learning　utilized　in　training　the　target　domain　data.　Experimental　results　show　that　the　proposed　method　achieves　92.12%　accuracy　for　recognition　of　activities　in　the　target　domain　by　migrating　the　source　and　target　domains.　The　proposed　approach　adapts　superior　to　cross-domain　scenarios　compared　to　the　existing　deep　learning　methods,　and　it　provides　a　low　cost　yet　highly　adaptable　solution　for　cross-domain　scenarios.　©　2014　IEEE.