Finger Vein Recognition Based on a Histogram of Competitive Gabor Directional Binary Statistics

Introduction

Finger veins are a body feature that is difficult to forge and is highly secure (Lu et al.,2018). Referencing other biometric features, such as fingerprints (Yang et al.,2022), palm prints (Jia et al.,2013), and faces (Lei et al.,2010), vein features are used for living body recognition and are not easily damaged, so finger vein identification has gradually become a research hotspot.

The finger vein recognition process usually consists of three stages: (1) preprocessing, including extracting a region of interest and image enhancement (Qu et al.,2022), (2) extracting features (Zhai et al.,2022), and (3) matching and identification, feature vectors are matched between test samples and training samples and then features are efficiently classified and recognized. The most important step is feature extraction, which has a great effect on recognition performance.

Recently, many studies have been performed on feature extraction methods for finger veins. Lu et al. (2014) proposed the competitive histogram representation, called HCGR, which makes full use of the Gabor filter with the ability to acquire image structural features from different directions, generates CGM and CGO images, and constructs feature histograms, which help to accurately represent finger vein orientation and texture characteristics. Yang et al. (2017) proposed adaptive vector field estimation, which is a feature representation method. By designing a spatial curve filter with variable curvature and direction, the vein curve is fitted to obtain the curve characteristics. Wang et al. (2019) proposed the DCGWLD and constructed a new variable curvature Gabor filter to replace the gradient descriptor in the original WLD. The extracted features not only have vein orientation characteristics but also reflect the degree of vein curvature. Tao et al. (2020) proposed the discriminative local descriptor AWASTP, which constructs an anisotropic Laplacian Gaussian operator, and proposed an anisotropic Weber local descriptor, which can obtain richer light-insensitive features and detailed information to enhance identification. These methods can extract local discriminative structural information through the texture variation and orientation features in finger vein images, but the images captured through the device are susceptible to illumination, translation, noise, and rotation. How to extract more discriminative structural features from finger vein images will be explored in-depth in the next work.

Inspired by HCGR and LBP, to solve the problems faced by the Gabor filter and LBP, the existing method based on the Gabor filter cannot distinguish finger vein images well; that is, the extracted features are not discriminative enough to reflect their rich structural information. Moreover, LBP and its various improvements cannot effectively solve image noise and rotation problems. This paper proposes an efficient image discriminative representation called the histogram of competitive Gabor directional binary statistics (HCGDBS). It filters the finger vein image through a Gabor filter and obtains the maximum response value as its dominant direction to overcome image noise and rotation. According to the dominant direction, the filter value of each direction is cyclically shifted, the LBP is improved by considering the intensity sequence difference relationship between the adjacent three directions of each pixel point, and the highly discriminative feature of the image is constructed. The experimental results of this paper fully reveal that this method outperforms Gabor, LBP, and various improved methods based on them.

These contributions are as follows: