This　study　presents　a　novel　approach　for　joint　gesture　and　identity　recognition　utilizing　Frequency-Modulated　Continuous　Wave　(FMCW)　radar.　The　proposed　approach　leveraging　a　custom-designed　two-stream　neural　network　(GI-Radar)　to　fuse　time-varying　range　and　angle　information.　In　the　network,　we　add　the　Convolutional　Block　Attention　Module　(CBAM)　to　enhance　the　feature　extraction　capability　of　network　in　both　spatial　and　channel　dimensions.　Experimental　results　show　that　the　accuracy　of　gesture　recognition　and　identity　recognition　of　this　method　can　reach　up　to　90%　and　93%　respectively,　which　is　superior　to　traditional　methods　and　proves　that　it　is　a　promising　wireless　solution.