A　real-time　mouse　cursor　control　system　using　static　gestures　was　designed　to　achieve　the　goal　of　human-computer　interaction　(HCI)　in　this　paper.　The　system　uses　the　computer＇s　own　camera　or　external　USB　camera　to　collect　video　data,　detect　and　recognize　gestures　of　people　in　the　video,　and　control　the　cursor　movement　or　click　in　real　time　based　on　the　gesture.　To　improve　the　recognition　accuracy　of　hand　gesture　images　detection,　a　method　of　hand　gesture　images　detection　based　on　PHOG　+　Improved　LBP　+　K-NN　was　proposed　in　this　paper.　In　order　to　improve　the　real-time　performance　of　the　system,　the　system　determines　whether　the　current　frame　has　human　hands　by　skin　color　detection.　When　human　hands　are　detected,　Pyramid　Histogram　of　Oriented　Gradients　(PHOG)　features　and　the　improved　Local　Binary　Pattern　(LBP)　features　are　further　extracted.　After　fusing　PHOG　features　and　improved　LBP　features,　k-nearest　neighbor　classification　(K-NN)　is　used　to　implement　gesture　recognition.　Six　different　gestures　were　tested　50　times　with　different　angles,　different　lights　and　no　skin　tone　in　the　background.　The　experimental　results　show　that　the　system　has　good　recognition　performance.　©　Published　under　licence　by　IOP　Publishing　Ltd.