Evaluation of multimodal biometric score fusion rules under spoof attacks

Recent works have shown that multimodal biometric systems can be evaded by spoofing only a single biometric trait. In this paper, we propose a method to evaluate the robustness of such systems against spoofing attacks, when score-level fusion rules are used. The aim is to rank several score-level fusion rules, to allow the designer to choose the most robust one according to the model predictions. Our method does not require to fabricate fake biometric traits, and allows one to simulate different possible spoofing attacks using the information of genuine and impostor distributions. Reported results, using data set containing realistic spoofing attacks, show that our method can rank correctly score-level fusion rules under spoofing attacks.