| Title |
CausaOne-sign: Causal explainable one-shot signature verification with lightweight cross-modality fusion |
| Authors |
Tehsin, Sara ; Nasir, Inzamam Mashood ; Hassan, Ali ; Riaz, Farhan |
| DOI |
10.1016/j.asej.2026.104002 |
| Full Text |
|
| Is Part of |
Ain Shams Engineering journal.. Amsterdam : Ain Shams University. 2026, vol. 17, iss. 2, art. no. 104002, p. 271-286.. ISSN 2090-4479. eISSN 2090-4495 |
| Keywords [eng] |
Causal explainability ; Graph-based representation ; Lightweight deep learning ; Meta-learning ; Offline signature verification ; One-shot learning |
| Abstract [eng] |
Background: Offline handwritten signature verification remains a difficult biometrics problem due to large intra-writer variability; skilled forgers; the limited number of reference samples available; and the black-box nature of many current deep learning based decision-making methodologies. Objective: To develop an interpretable, efficient one-shot learning framework that can perform offline signature verification for individuals who have never been seen before using as few reference signatures as possible. Materials and Methods: The proposed CausaOne-Sign model uses stroke aware graph encoding, transformer based reasoning, and prototypical embeddings, along with a causal attribution model to provide an explanation of how signature verification works. Experiments have been conducted using CEDAR, SigComp2011 UTSig, and BHSig260 datasets. Results: CausaOne-Sign achieved up to 97.4% accuracy and 99.1% area under the curve (AUC), with low ERR (1.8%), outperforming or matching state-of-the-art methods. Conclusion CausaOne-Sign offers a robust, interpretable, and resource-efficient solution for OSV, suitable for forensic and mobile applications. |
| Published |
Amsterdam : Ain Shams University |
| Type |
Journal article |
| Language |
English |
| Publication date |
2026 |
| CC license |
|
