Online Signature Verification based on User-generic Fusion Model with Markov Chain Monte Carlo Method

Personal authentication is becoming increasingly important. Biometrics, that is, the use of biological information, is one of the most promising techniques for this application. This paper proposes an online signature verification system. A serious problem in online signature verification is the difficulty of collecting enough signature data to generate a reliable model. In this paper, we propose a user-generic fusion model to resolve this problem. In the model generation, we use available datasets composed of genuine and forged signatures of many signers. The model's parameters are trained using the Markov chain Monte Carlo method. We report experimental results of our proposed algorithm using two public databases