Signature Verification Based On The Control Theory Of Skilled Movements

Dynamic signature verification based on temporal features are more precise than the static methods because in addition to position information of the drawing pattern, it uses local and global features extracted from velocity, acceleration, pressure and pen angle signals, while static methods only use image information. In this study, we segmented the signature patterns using the basic role of velocity in the control process of skilled movements and then the function features were extracted. In order to signal the matching evaluation, we applied five generalized functions and five weighting strategies for score level fusion. The results showed that the correlation criterion had the minimum error. The experiments on the database, consisting of persons of Persian, Chinese and English, showed that the skilled forgeries obtained an equal error rate (EER) of 0.87% and 1.24% for the user and universal thresholds, respectively.