One vs. One Offline Signature Verification: A Forensic Handwriting Examiners Perspective

Abstract

Verifying the authorship of a questioned signature is a common task for forensic handwriting examiners. While the automatic systems are typically orientated to improve performance, their practical utility for forensics is not always guaranteed. In this paper, we propose an offline automatic signature verifier oriented to forensic handwriting examiners. Our design is based on likelihood ratios which translate the signature verification results into objective and understandable evidence for a jury in a courtroom. The likelihood ratios depend on a universal background model build with signatures from other users and distance measures between signature handcrafted features. These features can be more interpretable for forensics, even though others can be included in our verifier, like deep learning ones. In our experiments, a single signature was used as reference. Two universal background models have been developed - the first is based on the GPDS database, and the second on synthetic signatures. The scheme is tried and tested with signatures from MCYT75 and BiosecureID databases with promising results. The outcome of this work is an offline signature verifier for forensic handwriting examiner practice.