Differential Feature Analysis for Palmprint Authentication

摘要

Palmprint authentication is becoming one of the most important bio-metric techniques because of its high accuracy and ease to use. The features on palm, including the palm lines, ridges and textures, etc., are resulted from the gray scale variance of the palmprint images. This paper characterizes these variance using different order differential operations. To avoid the effect of the illumination variance, only the signs of the pixel values of the differential images are used to encode palmprint to form palmprint differential code (PDC). In matching stage, normalized Hamming distance is employed to measure the similarity between different PDCs. The experimental results demonstrate that the proposed approach outperforms the existing palmprint authentication algorithms in terms of the accuracy, speed and storage requirement and the differential operations may be considered as one of the standard methods for palmprint feature extraction.