A new palmprint recognition method based on stochastic non-negative independent component analysis(SN-ICA) is proposed.In order to reduce computational complexity,the principal component analysis(PCA) is used to eliminate the second-order dependencies in the palmprint images.The remaining higher-order dependencies are separated by SN-ICA.First,the PCA and SN-ICA are applied to extract the projection vectors.Then the training images and test images are projected onto the projection vectors to get the palmprint feature vectors.Finally,the cosine distance between two feature vectors is calculated to match palmprint.The new algorithm is tested in PolyU plmprint database.The results show that compared with principal component analysis(PCA),two-dimensional principal component analysis(2D-PCA),independent component analysis(ICA) and two-dimensional independent component analysis(2D-ICA),the recognition rate of the new algorithm is the highest which is 98.95%,and the total time for feature extraction and matching is 0.564 s,so it meets the real-time system specification.