Double Gabor Orientation Weber Local Descriptor for Palmprint Recognition

In order to improve the palmprint recognition rate, this paper improves differential excitation and gradient orientation of Weber Local Descriptor (WLD) based on the texture features of palmprint images, and proposes a Double Gabor orientation Weber Local Descriptor (DGWLD). The directional information of the difference between the neighborhood pixels and the central pixel is considered to enlarge the difference between palmprint, when constructing the new differential excitation map. At the same time, gradient orientation is replaced by double Gabor orientation to reduce the influence of translation and rotation. In addition, a feature cross matching algorithm is used for further improve the recognition rate. Experiments on PolyU, MSpalmprint and CASIA palmprint databases show that the recognition rate is up to 100%. The experimental results show that the proposed method is superior in terms of identification rate and equal error rate compared with other local descriptor methods and improved WLD methods.