Analysis of user-specific score characteristics for spoof biometric attacks

Several studies in biometrics have confirmed the existence of user-specific score characteristics for genuine and zero-effort impostor score distributions. As an important consequence, biometric users contribute disproportionately to the FRR (false reject rate) and FAR (false accept rate) of the system. This phenomena is also know as the Doddington zoo effect. Recent studies indicate the vulnerability of unimodal and multibiometric systems to spoof attacks. The aim of this study is to analyze the score characteristics for spoof attacks. Such an analysis will 1) help improve our understanding of the Doddington zoo effect under spoof attacks; and 2) allow us to design biometric classifiers that are more robust to such attacks. The contributions of this paper are as follows: a) examining the existence of user-specific score characteristics for spoof attacks and b) analyzing the correlation between user-specific score characteristics obtained on genuine (as well as zero-effort impostor) and non zero-effort impostor (spoof) score distributions. Experiments conducted on the LivDet09 spoofed fingerprint database confirms that biometric user-groups exhibit different degrees of vulnerability to spoof attacks as well. Further, moderate negative correlation may exist between users who are difficult to recognize and their vulnerability to spoof attacks.