Cancelable approaches for biometric person authentication have been studied to protect enrolled biometric data, and several algorithms have been proposed. One drawback of cancelable approaches is that the performance is inferior to that of non-cancelable approaches. In this paper, we propose a scheme to improve the performance of a cancelable approach for online signature verification. Our scheme generates two cancelable dataset from one raw dataset and uses them for verification. Preliminary experiments were performed using a distance-based online signature verification algorithm. The experimental results show that our proposed scheme is promising.

The security of biometric authentication systems against impersonation attack is usually evaluated by the false accept rate, FAR. The false accept rate FAR is a metric for zero-effort impersonation attack assuming that the attacker attempts to impersonate a user by presenting his own biometric sample to the system. However, when the attacker has some information about algorithms in the biometric authentication system, he might be able to find a “strange” sample (called a wolf) which shows high similarity to many templates and attempt to impersonate a user by presenting a wolf. Une, Otsuka, Imai [22],[23] formulated such a stronger impersonation attack (called it wolf attack), defined a new security metric (called wolf attack probability, WAP), and showed that WAP is extremely higher than FAR in a fingerprint-minutiae matching algorithm proposed by Ratha et al. [19] and in a finger-vein-patterns matching algorithm proposed by Miura et al. [15]. Previously, we constructed secure matching algorithms based on a feature-dependent threshold approach [8] and showed that if the score distribution is perfectly estimated for each input feature data, then the proposed algorithms can lower WAP to a small value almost the same as FAR. In this paper, in addition to reintroducing the results of our previous work [8], we show that the proposed matching algorithm can keep the false reject rate (FRR) low enough without degrading security, if the score distribution is normal for each feature data.