2D-Gabor　transforms　are　considered　as　an　effective　spatial-frequency　analysis　technique　in　diverse　area　of　image　processing,　especially　in　texture　feature　detection　field　due　to　it　owns　good　localization　ability　in　both　spatial　and　frequency　domain　and　also　has　excellent　directional　selectivity.　In　this　paper,　a　method　of　feature　extraction　of　palm　print　using　real-Gabor　transform　(RGT)　is　proposed,　which　converts　the　spatial　domain　information　of　palm　print　to　joint　spatial-frequency　domain.　In　critical　sampling　case,　by　calculating　the　compactly　distributed　coefficients　of　RGT,　the　sub-block　energy　distribution　of　palm　print　in　spatial-frequency　domain　are　extracted　as　recognition　features.　Experimental　results　show　that　this　kind　of　feature　has　satisfactory　discrimination.　The　proposed　feature　extraction　method　has　low　computational　complexity　and　is　highly　suitable　for　palm　print　recognition　due　to　the　time-saving　operation.　It　can　achieve　high　verification　accuracy　and　has　favorable　robustness　against　small-scale　changes　and　angle　rotation　when　using　different　sampling　intervals.　©　2010　IEEE.