ABSTRACT In this paper, we propose a new local discriminative feature learning method for finger vein recognition. Unlike most previous finger vein recognition methods, which use hand-crafted descriptors such as local binary pattern, local line binary pattern, and Gabor features, this paper aims to learn a feature mapping to enhance the discriminative ability of local features. To achieve this goal, we first extract multi-directional pixel difference vectors (MDPDVs) for each pixel in a training finger vein image by computing the difference between each pixel and its straight-line neighboring pixels. Second, we learn a feature mapping to project these MDPDVs into low-dimensional binary codes in a supervised manner, where: 1) the loss between the original real-valued codes and learned binary vectors is minimized; 2) the between-class variation of the local binary features is maximized; and 3) the within-class variation of the local binary features is minimized. Last, we represent each finger vein image as a histogram feature by clustering and pooling these binary codes. Experiments on SDUMLA-FV and PolyU databases verify the superior performance of the proposed method over other existing finger vein recognition methods. INDEX TERMS Finger vein recognition, feature learning, local line binary pattern, discriminative binary descriptor, biometrics.
To learn a feature mapping relationship to enhance the recognition ability of local features, a discriminative binary descriptor (DBD) REF method is proposed.
Discriminative Binary Descriptor for Finger Vein Recognition
IEEE Access
IEEE Access
Computer Science