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

Huabin Wang,Mengli Du,Liang Tao,Jian Zhou

doi:10.1109/access.2019.2928472

Huabin Wang, Mengli Du + Show 2 more

Open Access

https://doi.org/10.1109/access.2019.2928472

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Abstract

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.

Highlights

As a convenient biometric recognition technology, finger vein recognition has attracted extensive attention over the past few years
In this paper, we have improved the Weber local descriptor (WLD), because the original WLD has the following problems when it is applied for finger vein recognition: 1) When calculating the differential excitation, it sums the grey differences between different neighborhood pixels and the center pixel in each local region; the directions associated with these differences are neglected
This is because the proposed filters simultaneously consider both the direction and curvature of the finger veins and can better match the line features in a finger vein image

Summary

INTRODUCTION

As a convenient biometric recognition technology, finger vein recognition has attracted extensive attention over the past few years. Rosdi et al [16] proposed a local line binary pattern (LLBP) descriptor, which treats the neighborhood shape as a line to obtain more stable features This method has the advantage of accurately describing the orientation information of finger veins; in cases of noise in the image or image rotation, this method cannot effectively extract features. There are many bifurcation points in these line features Motivated by these observations, in this paper, we have improved the WLD, because the original WLD has the following problems when it is applied for finger vein recognition: 1) When calculating the differential excitation, it sums the grey differences between different neighborhood pixels and the center pixel in each local region; the directions associated with these differences are neglected.

RELATED WORK

EXPERIMENTAL ANALYSIS

MATCHING AND RECOGNITION

7: H1 and H2 are normalized using the l2 norm to obtain

Findings

CONCLUSION