Online Signature Verification Based on User-Generic Fusion Model with Markov Chain Monte Carlo, Taking into Account User Individuality

Personal authentication is becoming an increasingly important problem. Online signature verification is one promising form of biometric authentication. However, the verification accuracy of online signature verification is not high enough and still needs to be improved. To do so, 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, to further improve the verification accuracy, we propose a method that can take such individuality into account. First, in a training phase, we divide a training dataset into several groups. Then, several fusion models are generated using a parameterized family of nonlinear functions by a Markov chain Monte Carlo method. In an enrollment phase, in order to take into account users’ individuality, we introduce model reliability. The model reliability for each user and each model is different, enabling us to take users’ individuality into account. In a verification phase, a marginal likelihood is calculated for each group. Then, a verification score is calculated by a weighted sum of the marginal likelihoods from the groups, using the model reliability. To evaluate the performance of the proposed algorithm, we conducted experiments using the SVC2004 database. The verification accuracy was improved over the previous algorithm.