Biometric Identification through Hand Vein Patterns

Abstract

Vein pattern is the vast network of blood vessels underneath a person's skin. These patterns in the hands are assumed to be unique to each individual and they do not change over time except in size. The properties of uniqueness, stability and strong immunity to forgery of the vein patterns make it a potentially good biometric trait. In this study, we present a novel biometric technique based on the statistical processing of the hand vein patterns. We considered the performance of four alternative feature sets and explored their fusion. The performance of the proposed algorithms was tested on a database of hand veins captured in the near infrared band and collected from 100 people. Our data collection is more realistic in that subjects had to undergo the procedures of holding a bag, pressing an elastic ball and cooling with ice, all exercises that force changes in the vein patterns. Our hand vein biometric tool outperforms the nearest competitor; furthermore tests of simulating real life conditions reveal the fusion scheme to be adequately robust.