Given the advantages of low computation cost and absence of a training requirement

local descriptor-based palmprint recognition methods are eliciting an increasing amount of attention. The Weber local descriptor (WLD) is a newly presented local descriptor inspired by Weber's law in psychology. This study applies WLD to palmprint recognition. To improve palmprint recognition performance

a line feature WLD is presented by considering the sufficient line features of a palmprint. First

modified finite random transform or the Gabor filter is applied to a palmprint image to generate directional image and energy image E. Second

energy image E is convoluted by the Weber operator to generate differential excitation image . Finally

based on directional image and differential excitation image

the histogram of the line Weber local feature can be constructed for use in palmprint recognition. The Polytechnic University Palmprint Database II and the Cross-Sensor Palmprint Database are utilized in an experiment on Polyu Ⅱ database. The proposed method can achieve 100% identification rate with both Manhattan and chi-square distance. Results demonstrate that the presented method has a high identification rate and is robust. This study introduces WLD into palmprint recognition to develop a new palmprint recognition method based on a local descriptor. Compared with other palmprint recognition methods based on a local descriptor

the presented method has a higher identification rate and is more robust.