Finger-vein image recognition combining modified Hausdorff distance with minutiae feature matching

Abstract

In this paper, we propose a novel method for finger-vein recognition. We extract the features of the vein patterns for recognition. Then, the minutiae features included bifurcation points and ending points are extracted from these vein patterns. These feature points are used as a geometric representation of the vein patterns shape. Finally, the modified Hausdorff distance algorithm is provided to evaluate the identification ability among all possible relative positions of the vein patterns shape. This algorithm has been widely used for comparing point sets or edge maps since it does not require point correspondence. Experimental results show that these minutiae feature points can be used to perform personal verification tasks as a geometric representation of the vein patterns shape. Furthermore, by this developed method, we can achieve robust image matching under different lighting conditions.

Iris and Finger Vein Multi Model Recognition System Based on Sift Features

“…3(b). To obtaining the structure of the finger vein network, an image thinning method proposed in [11] is utilized to obtain the skeleton of finger veins. Some finger vein skeletons are presented in Fig.…”

A Robust Technique to Characterize the Palmprint using Radon transform and Delaunay triangulation

“…The Hausdorff distance provides a measure between two point sets. Unlike most of shape recognition techniques which require a one-toone correspondence between the template and the testing data, the Hausdorff distance can be found without explicit point correspondence [3,[5][6][7]11].…”

“…Hausdorff distance is the maximum distance of a set to the nearest point in the other set [3,[5][6][7]11]. Mathematically, Hausdorff distance from set to set is a maximum function defined as: (11) Where , where the centroids are extracted from Radon coefficients and can be any metric between these points.…”

Finger Vein Recognition Using 2DGabor Combined With OECA

“…feature can be divided into four classes: the contour points feature, the venous lines feature, the texture feature and the feature obtained by machine learning methods. The contour point feature is the earliest applied to finger vein recognition to extract the bifurcations point and the endpoints as the classification information [5], and then the SIFT (Scale-invariant feature transform) feature points are introduced to enrich the robustness against rotation [6], but the contour point feature will lose a lot of information. Finger vein line can reflect the vein topology, linear tracking method [7], regional growth method [8], curvature method [9] is often used to extract the vein line.…”