Noise　and　constant　empirical　motion　constraints　affect　the　extraction　of　distinctive　spatiotemporal　features　from　one　or　a　few　samples　per　gesture　class.　To　tackle　these　problems,　an　adaptive　local　spatiotemporal　feature　(ALSTF)　using　fused　RGB-D　data　is　proposed.　First,　motion　regions　of　interest　(MRoIs)　are　adaptively　extracted　using　grayscale　and　depth　velocity　variance　information　to　greatly　reduce　the　impact　of　noise.　Then,　corners　are　used　as　keypoints　if　their　depth,　and　velocities　of　grayscale　and　of　depth　meet　several　adaptive　local　constraints　in　each　MRoI.　With　further　filtering　of　noise,　an　accurate　and　sufficient　number　of　keypoints　is　obtained　within　the　desired　moving　body　parts　(MBPs).　Finally,　four　kinds　of　multiple　descriptors　are　calculated　and　combined　in　extended　gradient　and　motion　spaces　to　represent　the　appearance　and　motion　features　of　gestures.　The　experimental　results　on　the　ChaLearn　gesture,　CAD-60　and　MSRDailyActivity3D　datasets　demonstrate　that　the　proposed　feature　achieves　higher　performance　compared　with　published　state-of-the-art　approaches　under　the　one-shot　learning　setting　and　comparable　accuracy　under　the　leave-one-out　cross　validation.