In　recent　years,　radar-based　hand　gesture　recognition　(HGR)　has　attracted　much　attention　in　the　field　of　human-computer　interaction　(HCI)　due　to　its　benefits　of　high　recognition　accuracy　and　independence　from　lighting　conditions.　Conventional　deep　learning　(DL)　based　models　for　HGR　rely　on　a　large　amount　of　labelled　data　for　training　to　achieve　a　satisfactory　recognition　accuracy.　However,　it　is　usually　time-consuming　and　labor-intensive　for　collecting　large　amounts　of　radar　data　and　also　difficult　to　cover　all　possible　hand　gesture　classes.　In　this　paper,　we　introduce　meta-learning　to　address　the　few-shot　learning　problem　for　frequency　modulated　continuous　wave　(FMCW)　radar　based　HGR.　We　propose　a　meta-learning　network　to　learn　a　model　that　can　quickly　adapt　to　unseen　hand　gesture　tasks　with　few　training　observations.　In　particular,　we　propose　a　3D　convolutional　neural　network　(CNN)　based　dual-channel　fusion　network　for　feature　extraction　by　exploiting　the　correlations　of　multiple　features　in　radar　echo　signals　to　improve　the　recognition　accuracy.　In　addition,　we　also　develop　a　learnable　relation　module　with　neural　networks　as　a　non-linear　classifier　to　measure　the　similarity　between　the　samples　of　different　hand　gestures,　which　can　be　more　applicable　for　the　HGR　task.　Finally,　we　evaluate　the　performance　of　the　proposed　model　by　conducting　experiments　on　an　FMCW　radar　hardware　system.　Experimental　results　show　that　the　proposed　meta-learning　model　substantially　enhances　the　recognition　accuracy　compared　with　the　state-of-the-art　methods　including　DL　and　meta-learning　based　models　for　FMCW　radar-based　HGR.　©　2001-2012　IEEE.