This　paper　proposes　a　novel　moving　hand　segmentation　approach　using　skin　color,　grayscale,　depth,　and　motion　cues　for　gesture　recognition.　The　proposed　approach　does　not　depend　on　unreasonable　restrictions,　and　it　can　solve　the　problem　of　hand-over-face　occlusion.　First,　an　online　updated　skin　color　histogram　(OUSCH)　model　is　built　to　robustly　represent　skin　color;　second,　according　to　the　variance　information　of　grayscale　and　depth　optical　flow,　a　motion　region　of　interest　(MRoI)　is　adaptively　extracted　to　locate　the　moving　body　part　(MBP)　and　reduce　the　impact　of　noise;　then,　Harris-Affine　corners　that　satisfy　skin　color　and　adaptive　motion　constraints　are　adopted　as　skin　seed　points　in　the　MRoI;　next,　the　skin　seed　points　are　grown　to　obtain　a　candidate　hand　region　utilizing　skin　color,　depth　and　motion　criteria;　finally,　boundary　depth　gradient,　skeleton　extraction,　and　shortest　path　search　are　employed　to　segment　the　moving　hand　region　from　the　candidate　hand　region.　Experimental　results　demonstrate　that　the　proposed　approach　can　accurately　segment　moving　hand　regions　under　different　situations,　especially　when　the　face　is　occluded　by　a　hand.　Furthermore,　this　approach　achieves　higher　segmentation　accuracy　than　other　state-of-the-art　approaches.　©　Allerton　Press,　Inc.,　2017.