抄録
Personal authentication is becoming an increasingly important problem. Online signature verification is one promising form of biometric authentication. To improve the accuracy of online signature verification, we previously proposed a user-generic fusion model. Although the verification accuracy was reasonably good, the proposed method cannot adequately take into account users' individuality. In this paper, we propose a method that can incorporate users' individuality. First, we divide a training dataset into several groups by the K-means method. Then a model is generated from each group using a parameterized family of nonlinear functions. In the testing phase, a marginal likelihood is calculated for each group by a Markov chain Monte Carlo method. In order to take into account users' individuality, a verification score is calculated by a weighted sum of the marginal likelihoods from the groups. To evaluate the performance of the proposed algorithm, we conducted experiments using the SVC2004 database. The verification accuracy was improved over the previous algorithm.
