| dc.description.abstract | In this article, a new approach to offline signature verification, based on
a general-purpose wide baseline matching methodology, is proposed. Instead of
detecting and matching geometric, signature-dependent features, as it is usually
done, in the proposed approach local interest points are detected in the signature
images, then local descriptors are computed in the neighborhood of these points,
and afterwards these descriptors are compared using local and global matching
procedures. The final verification is carried out using a Bayes classifier. It is important
to remark that the local interest points do not correspond to any signaturedependent
fiducial point, but to local maxima in a scale-space representation of
the signature images. The proposed system is validated using the GPDS signature
database, where it achieves a FRR of 16.4% and a FAR of 14.2%. | |
