Text-indicated Writer Verification Using Hidden Markov Models

We propose an HMM-based text-indicated writer verification method, which is based on a challenge and response type of authentication process. In this method, a different text including ordinary characters is used on every occasion of verification. This text can be selected automatically by the verification system so as to reflect a specific writer's personal features. The specific writer is accepted only when the same text as indicated by the verification system is inputted, and the system can verify the writer's personal features from the inputted text. Moreover, the characters used in the verification process can be different from those in the enrollment process. This method makes it more difficult to get away with forged handwriting than the previous methods using only signatures. In the proposed method, the characteristics of the indicated text and each writer's personal features are both represented by using Hidden Markov Models