Multimodal Biometric Fusion: Performance under Spoof Attacks

Abstract

Biometrics is essentially a pattern recognition system that recognizes an individual using their unique anatomical or behavioral patterns such as face, fingerprint, iris, signature etc. Recent researches have shown that many biometric traits are vulnerable to spoof attacks. Moreover, recent works showed that, contrary to a common belief, multimodal biometric systems in parallel fusion mode can be intruded even if only one trait is spoofed. However, most of the results were obtained using simulated spoof attacks, under the assumption that the spoofed and genuine samples are indistinguishable, which may not be true for all biometric traits. In addition, so far vulnerability of multimodal biometric systems in serial fusion mode against spoof attacks has not been investigated. These issues raise a demand to investigate the robustness of multimodal systems under realistic spoof attacks. In this paper, we empirically investigate the performance of serial and parallel biometric fusion modes under realistic spoof attacks. Preliminary empirical results on real biometric systems made up of face, fingerprint and iris confirm that multimodal biometric systems in both fusion modes are not intrinsically robust against spoof attacks as believed so far. In particular, multimodal biometric systems in serial fusion mode can be even less robust than systems in parallel mode. We also experimentally found that incorporating the biometric sample quality in biometric fusion increases the robustness of the multimodal systems against spoof attacks. In the end, we study the trade-off between performance and robustness of the biometric systems under spoof attacks.