Vascular biometrics based on a minutiae extraction approach

Abstract

In this paper, the authors will describe a new algorithm based on minutiae extraction which is inspired by currently fingerprint systems, but adapted to the own characteristics of vein patterns. All the steps of the system, from the image pre-processing to the comparison algorithm, are described, including also the biometric feature extraction process. After describing the system, some obtained results are detailed. The algorithm proposed has been tested with two different databases: one database acquired by the authors, using a self-designed sensor; and one semi-publicly accessible database. These results do not only show the low error rates obtained but also the universality of the proposed system, as well as the ease of adapting the algorithm to cope with the different characteristics of each database

Wrist Vascular Biometric Recognition Using a Portable Contactless System

“…Vascular Biometrics Based on a Minutiae Extraction Approach [8] (2011) Spectral Minutiae for Vein Pattern Recognition [14] (2011)…”

“…Fast Cross-Correlation Based Wrist Vein Recognition Algorithm with Rotation and Translation Compensation [13] (2018) Dataset Name Singapore (NIR, Own) [9] UC3M (Own) [8] and Singapore [5] UC3M [8] and Singapore [9] PUT (Public) [6] PUT […”

“…It was collected with the proposed TGS-CVBR ® , and the hardware described previously. The two other databases detailed in Table 1 and acquired with physical contact, UC3M [8] and PUT [6], were employed in this study in order to compare, with contact and non-contact dataset, the results obtained with the software algorithms proposed: TGS-CVBR ® and PIS-CVBR ® .…”

Feature extraction from vein images using spatial information and chain codes

“…Local Methods Known as established features from fingerprints, minutiae have also been used for extracting features from skeletonized vein images [28]. Because minutiae are composed of spatial coordinates, they are subject to translation and rotation.…”

“…This issue is addressed by projecting minutiae points into frequency space [34], where translation gets eliminated and rotation becomes translation. Spectral minutiae have also been applied to vein recognition [28] in different variants. SML performs an element-wise comparison of two minutiae-spectra in frequency space, whereas SML fast Rotate (SMLFR) compares the spectra while trying different translations of them.…”