Finger Vein Feature Extraction Based on Improved Maximum Curvature Description

In this paper, we try to solve the problem that it is difficult to distinguish between the background and foreground regions in vein images, and improve the feature extraction of finger veins by using maximum curvature method (MCM), which is easily affected by noise. In this paper, we use the improved maximum curvature method to extract finger vein features. Different from the maximum curvature method (MCM), we try an enhancement mechanism to improve the anti-noise ability. Firstly, the improved mask filtering method is used to locate the finger region, which can effectively separate the foreground region from the background region. When it comes to feature extraction, the Gabor wavelet transform is used to remove the noise of the image, and the basic gray-level grouping (GLG) method is used to increase the contrast of the image. Finally, the vein width is obtained by the maximum curvature method. The method is based on two datasets, namely, MMCBNU 6000 finger vein database and SDUMLA-HMT finger vein database. Experiments show that the recognition rates of the two databases are 99.27% and 98.13% respectively.