The　newly　emerging　hand　vein　recognition　technology　has　attracted　remarkable　attention　for　its　uniqueness,　noninvasion,　friendliness　and　high　reliability.　It　is　unavoidable　to　produce　small　location　deviation　of　human　hand　in　the　practical　application;　however,　the　existing　recognition　methods　are　sensitive　to　the　hand　shift　or　rotation.　The　test　sample　is　matched　with　a　series　of　registered　images　after　affine　transformation　including　the　shift　or　rotation　by　most　of　researches,　this　affine　transform　method　can　remedy　the　location　deviation　to　some　extent,　but　the　limited　range　for　hand　shift　and　rotation　brings　users　much　inconvenience　and　the　computational　cost　also　increases　greatly.　Aiming　at　this　issue,　a　hand　vein　recognition　algorithm　based　on　local　SIFT　(Scale　Invariant　Feature　Transform)　analysis　is　developed　in　this　contribution,　which　has　practical　significance　due　to　its　translation　and　rotation　invariance.　First,　the　hand　vein　image　is　preprocessed　to　remove　the　background　and　reduce　image　noises,　and　then　SIFT　features　are　extracted　to　describe　the　gradient　information　of　hand　vein.　Many　one-to-more　matching　pairs　are　produced　by　the　common　matching　method　of　SIFT　features,　thus　the　matching　rule　is　improved　by　appending　a　constrained　condition　to　ensure　the　one-to-one　matching,　which　is　achieved　by　selecting　feature　point　with　the　nearest　distance　as　the　optimal　match.　Finally　the　match　ratio　of　features　between　the　registered　and　test　images　is　calculated　as　the　similarity　measurement　to　verify　the　personal　identification.　The　experiment　results　show　that　FRR　(False　Rejection　Rate)　is　only　0.93%　when　FAR　(False　Acceptance　Rate)　is　0.002%,　and　EER　(Equal　Error　Rate)　is　low　to　0.12%,　which　demonstrate　the　proposed　approach　is　valid　and　effective　for　hand　vein　authentication.　©　2009　SPIE.