Explainable offline automatic signature verifier to support forensic handwriting examiners
This work addresses the need for explainable signature verification to support forensic handwriting examiners, though it is incremental as it builds on existing universal background model techniques.
The paper tackled the problem of signature verification systems being difficult to explain, which limits their acceptance in forensic and legal applications, by proposing an explainable offline automatic signature verifier that achieves competitive performance with state-of-the-art methods on publicly available databases.
Signature verification is a critical task in many applications, including forensic science, legal judgments, and financial markets. However, current signature verification systems are often difficult to explain, which can limit their acceptance in these applications. In this paper, we propose a novel explainable offline automatic signature verifier (ASV) to support forensic handwriting examiners. Our ASV is based on a universal background model (UBM) constructed from offline signature images. It allows us to assign a questioned signature to the UBM and to a reference set of known signatures using simple distance measures. This makes it possible to explain the verifier's decision in a way that is understandable to non experts. We evaluated our ASV on publicly available databases and found that it achieves competitive performance with state of the art ASVs, even when challenging 1 versus 1 comparison are considered. Our results demonstrate that it is possible to develop an explainable ASV that is also competitive in terms of performance. We believe that our ASV has the potential to improve the acceptance of signature verification in critical applications such as forensic science and legal judgments.