Abstract: Independent Component Analysis is undertaken for palmprint recognition not only to get the image of the higher order statistics information，but also to make each component as independent as possible． However．it needs a large quantity of calculation，and the classification feature is not obvious． An improved method is developed，based on ICA．First of all，wavelet transformtion was conducted to reduce dimension，which can guarantee minimum loss of image inform ation features，and at the same time， reduce sharply the computional cost．Then ICA method was employed to get the independent basis vectors． Finally，Fisher Linear Discriminant(FLD)method was used for feature extraction in the subspace fermed by the independent basis vectors to make the image have better classification information．Experimental results show that the improved method not only improves recognition rates，but also reduces recognition time．

Key words: palmpfint recognition；independent component analysis；fisher linear discriminant