Off-line skilled forgery detection using stroke and sub-stroke properties

Research has been active in the field of forgery 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.