A novel matching strategy for finger vein recognition

Authors:
Rongyang Xiao;Gongping Yang;Yilong Yin;Lu Yang
Affiliations:
School of Computer Science and Technology, Shandong University, Jinan, China;School of Computer Science and Technology, Shandong University, Jinan, China;School of Computer Science and Technology, Shandong University, Jinan, China;School of Computer Science and Technology, Shandong University, Jinan, China
Venue:
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2012

Citing 7
Cited 0

Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification

Machine Vision and Applications
Handbook of Multibiometrics (International Series on Biometrics)

Handbook of Multibiometrics (International Series on Biometrics)
Driver identification using finger-vein patterns with Radon transform and neural network

Expert Systems with Applications: An International Journal
Handbook of Fingerprint Recognition

Handbook of Fingerprint Recognition
Finger vein recognition with manifold learning

Journal of Network and Computer Applications
Finger-vein pattern identification using principal component analysis and the neural network technique

Expert Systems with Applications: An International Journal
A finger-vein verification system using mean curvature

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

Finger vein recognition is a promising biometric recognition technology, which verifies identities via the vein patterns in the fingers. The vein vessel network is a very important vein pattern for finger vein recognition. Based on this pattern, in the matching stage, the matched pixel ratio (MPR), Hamming distance (HD) and the mismatched ratio are commonly used as the matching algorithms to evaluate the similarity between two finger vein images. But these matching algorithms are calculated pixel by pixel, they are sensitive to the image translation and rotation. In this paper, a novel matching strategy region-based axis projection (RAP) is proposed for finger vein recognition. We first divide the vein pattern into small regions, then concatenate the projection of the vein distribution curves on the x-axis and y-axis of each region, and finally evaluate the similarity by calculating the projections of the whole vein pattern. Experimental results show that the proposed method can avoid image translation and rotation to some extent and achieve a better performance.