Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/128828
Título: Explainable offline automatic signature verifier to support forensic handwriting examiners
Autores/as: Díaz Cabrera, Moisés 
Ferrer Ballester, Miguel Ángel 
Vessio, Gennaro
Clasificación UNESCO: 331117 Equipos de verificación
330405 Sistemas de reconocimiento de caracteres
Palabras clave: Biometrics
Explainability
Forensic
Handwriting
Signature Verification, et al.
Fecha de publicación: 2024
Publicación seriada: Neural Computing and Applications 
Resumen: 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 comparisons 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.
URI: http://hdl.handle.net/10553/128828
ISSN: 0941-0643
DOI: 10.1007/s00521-023-09192-7
Fuente: Neural Computing and Applications [ISSN 0941-0643], v. 36, nº 5. p. 2411-2427, (Febrero 2024)
Colección:Artículos
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