Customized Local Line Binary Pattern Method for Finger Vein Recognition

Abstract

Finger vein images present plenty of oriented features. Local line binary pattern (LLBP) and its variance are very good oriented feature representation methods, but their discrimination may be limited, since they does not utilize the class labels in the process of extracting features. In this paper, a class based orientation-selectable PLLBP method, called customized local line binary pattern (CLLBP), is proposed for finger vein recognition. We first calculate the average genuine scores using components of PLLBP at different orientations for each class on the training set, respectively. Secondly, we sort these average genuine scores from the different orientations for each class to rank each component in their relative importance. Thirdly, we choose the k most important components at the top-k orientations for each class. Lastly, given a testing image and an enrolled image, we only use the components at the top-k orientations of the enrolled class to calculate the matching score. Experimental results on the PolyU database verify the better performance of the proposed method than other algorithms, such as LBP and LLBP.