Extensive Threat Analysis of Vein Attack Databases and Attack Detection by Fusion of Comparison Scores
This work addresses the problem of evaluating and improving attack detection in vein biometric systems for security applications, but it is incremental as it builds on existing databases and fusion methods.
The study conducted a systematic threat evaluation of three public finger vein and one private dorsal hand vein attack databases by testing 14 vein recognition schemes against attack samples, reporting the Impostor Attack Presentation Match Rate as the percentage of wrongly accepted attacks, and then used score level fusion to combine comparison scores for presentation attack detection.
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.