Palmprint identification, as an emerging biometric technique, has been actively researched in recent years. In existing palmprint identification algorithms, ROI segmentation is always a must step. This paper presents a novel hierarchical palmprint identification method without ROI extraction, which measures hand geometry and angle values in coarse-level feature extraction, and calculates unit information entropy of each subimage to describe grayscale distribution as the fine-level feature. We utilize the grayscale distribution variance caused by particular positions of principle lines, wrinkles and minutiae in primitive hand images as the palm descriptor instead of ROI-based features. Experiments were developed on a database of 990 images from 99 individuals. Accuracy up to 99.24% has been obtained when using 6 samples per class for training. A performance comparison between the proposed method and ROI-based PCA method was made also.