Weber Local Descriptors With Variable Curvature Gabor Filter for Finger Vein Recognition

Since captured finger vein images are usually of low quality, it is challenging to extract reliable finger vein features directly from the original finger vein images. Most current methods utilize texture change information and rich line features to extract features from finger vein images while neglecting the curvature of the finger veins. In this paper, a Weber local descriptor (WLD) with variable curvature Gabor filters is proposed for finger vein recognition. First, the differential excitation operator in the original WLD is improved by adding directional information, thereby allowing the local texture changes in an image to be better characterized and enhancing the differences between heterogeneous finger veins. Then, variable curvature Gabor filters are introduced to extract finger vein features that can simultaneously reflect the directional information and the curvature of the finger veins. In fact, two response values from the proposed Gabor filters are employed as features for each pixel; this approach is equivalent to defining intervals for the line features, rather than single values, and makes the results more robust to the rotation. The extensive experiments on the SDUMLA-FV and PolyU databases demonstrate that the proposed method can effectively improve the performance of finger vein recognition and show good robustness to translation, rotation, and illumination.