Online signature verification using double-stage feature extraction modelled by dynamic feature stability experiment
Identification based on signature verification is one of the most popular biometric methods which were used even prior to the advent of computers. In the field of dynamic signature verification, signatures' time functions (e.g. pen tip velocity, acceleration and pressure) are analysed in addition to static appearance of signatures. Dynamic feature stability (DFS) experiment is a process for finding the most stable signature partitions which are difficult to forge. The experiment can most effectively lead to a focus on the signature trajectories. Due to the different angles related to the signature pad which are regarded as major problem in signature verification algorithms, in this study, radon transform is used to transform rotation effect to shift effect. Convolutional neural network disregards the precise location of image features, as well as shift effects in both axes of image that is decline by its nature. According to DFS experiment, three independent recognition paths are structured and their effects on final classification are determined by the experiment. Three Persian datasets are analysed by DFS, which led to a reliable trait of Persian signatures. As a result, the least verification error is attained. Besides, SVC2004, as an international benchmark is evaluated by the proposed algorithm.