Template Selection for On-line Signature Verification

In this paper, we propose two affective methods to perform automatic template selection where the goal is to select prototype signature templates for a user from a given set of online signatures. The first method employs a clustering strategy to choose a template set that best represents the intra-class variations, while the second method selects templates that exhibit maximum similarity with the rest of the signatures. In the experiment, two typical online signature verification have been employed, respectively based on global and local features, and the verifying results on a database Task2 of SVC2004 (First Signature Verification Competition 2004), with 20 genuine signatures and 20 skilled forgeries for each set, indicate that two proposed selection procedures as presented here results in better performance than random template selection.