  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.
