A Hierarchical Palmprint Identification Method Using Hand Geometry and Grayscale Distribution Features

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