Research has been active in the field offorgeiy detection, but relatively little work has been done on the detection of skilled forgeries. In this paper, we present an algorithm for detecting skilled forgeries based on a local correspondence between a questioned signature and a model obtained a priori. Writer-dependent properties are measured at the substroke level and a cost function is trained for each writer. When a candidate signature is presented, the same features are extracted and matched against the model. We present a description of the features and experimental results.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.