An increasing number of studies have reported that the quality of biometric samples has a significant impact on the performance of the system. However, to our best knowledge, these studies are limited to impersonation attempts from different subjects, i.e., zero-effort attack, and they do not take into account the possibility of spoof attack, also called non-zero effort attack. In order to thwart the spoof attack, one way is to assess the likelihood of a spoof attempt by using biometric liveness measures. Since both biometric sample quality and liveness measures are different, and possibly complementary, we propose an information fusion framework that combines them under both zero- and nonzero effort (spoof) attacks. We implemented this framework using three generative classifiers, namely, Gaussian Mixture Model, Gaussian Copula, and Quadratic Discriminant Analysis. Experimental results on LivDet 11 spoof fingerprint database demonstrate that the proposed framework can reduce the error rate of the baseline system by about 56%, under both types of attack.
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