Abstract: The research of previous palmprint identification algorithms are mainly focus on ROI districts cropped from the central part of palmprint images, but always ignore the color features like grayscale distribution. Under regular illumination, the texture and skin color of different position within a palmprint image will lead to differentiable grayscale distribution. In this paper, a novel hierarchical method of palmprint identification is presented, which extracts hand geometry and angle values as the coarse-level features, and calculates the unit information entropy of each subimage to describe the images grayscale distribution as the fine-level feature. Distinctive to other identification methods existed, the proposed method do not need to extract ROI districts but utilize the skin colors distinction caused by locations of principle lines, wrinkles and minutias. The experimental results on the database containing 990 images from 99 individuals show the effectiveness and robustness of the proposed method compared with the traditional method PCA and LDA.