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The vulnerability of finger vein recognition to spoofing is studied in this paper. A collection of spoofing finger vein images has been created from real finger vein samples. Finger vein images are printed using a commercial printer and then, presented at an open source finger vein sensor. Experiments are carried out using an extensible framework, which allows fair and reproducible benchmarks. Experimental results lead to a spoofing false accept rate of 86%, thus showing that finger vein biometrics is vulnerable to spoofing attacks, pointing out the importance to investigate countermeasures against this type of fraudulent actions.
Roger Hersch, Isaac Amidror, Patrick Emmel, Nicolas Rudaz