Extensive Threat Analysis of Vein Attack Databases and Attack Detection by Fusion of Comparison Scores

TL;DR

This paper systematically evaluates the threat level of various vein attack databases by testing multiple recognition schemes and explores fusion of comparison scores for attack detection.

Contribution

It provides a comprehensive threat analysis of publicly available vein attack databases and introduces a fusion-based approach for presentation attack detection.

Findings

High attack success rates on several databases

Fusion of scores improves attack detection accuracy

Identifies vulnerabilities in current vein recognition systems

Abstract

The last decade has brought forward many great contributions regarding presentation attack detection for the domain of finger and hand vein biometrics. Among those contributions, one is able to find a variety of different attack databases that are either private or made publicly available to the research community. However, it is not always shown whether the used attack samples hold the capability to actually deceive a realistic vein recognition system. Inspired by previous works, this study provides a systematic threat evaluation including three publicly available finger vein attack databases and one private dorsal hand vein database. To do so, 14 distinct vein recognition schemes are confronted with attack samples and the percentage of wrongly accepted attack samples is then reported as the Impostor Attack Presentation Match Rate. As a second step, comparison scores from different recognition schemes are combined using score level fusion with the goal of performing presentation attack detection.