A statistical approach for offline signature verification using local gradient features

Abstract

Signature is widely used as a means of personal verification which emphasizes the need for a signature verification system. Often the single signature feature may produce unacceptable error rates. Features used in this method are mainly local key-point feature that deals with the orientation around each key-point. Before extracting the features, preprocessing of a scanned image is necessary to isolate the region of interest part of a signature and to remove any spurious noise present. The system is initially trained using a database of signatures obtained from those individuals whose signatures are to be authenticated by the system. For extracting the feature, key-points of the image are detected. For each point, orientation around the point is calculated as the feature. By matching the features of sample signature and testing signature decision is taken. If a query signature is in the acceptance range then it is an authentic signature, otherwise it is a forged signature.