Local Orientation Binary Pattern with Use for Palmprint Recognition

Abstract

In this paper, we extensively exploit the discriminative orientation features of palmprint, including the principal orientation and corresponding orientation confidence, and further propose a local orientation binary pattern (LOBP) for palmprint recognition. Different from the existing binary based representation methods, the LOBP method first captures the principal orientation consistency by comparing the center point with the neighbor sets, and then captures the confidence variations by thresholding the center confidence with neighborhoods so as to obtain orientation binary pattern (OBP) and confidence binary pattern (CBP), respectively. Furthermore, the block-wise statistics of OBP and CBP are concentrated to generate a novel descriptor, namely LOBP, of palmprint. Experiment results on different types of palmprint databases demonstrate the effectiveness of the proposed method.