Robust Few-Shot Online Signature Verification via Bi-Directional-Guided Fusion and Padding-Aware Attention

Online signature verification (OSV) remains challenging under few-shot enrollment and multi-posture conditions, where limited reference samples and writing variations increase intra-writer variability and degrade verification performance. Existing methods often rely on simple feature concatenation and insufficiently exploit interactions between global statistical and temporal dynamic representations, while attention mechanisms in variable-length sequences may be affected by invalid padded regions. To address these limitations, this study introduces FT-Transformer, a hybrid CNN–Transformer framework for few-shot online signature verification. The model jointly encodes 18-dimensional local dynamic descriptors and 51-dimensional global statistical features. A bi-directional-guided fusion mechanism is introduced to facilitate interaction between heterogeneous representations, while a closed-loop feedback pathway and padding-aware attention masking are incorporated to improve feature robustness. Experiments on the public SVC2004 benchmark and a custom multi-posture dataset (MPIS-Sig) evaluate the performance of this framework. Under the WD-10/10 protocol, FT-Transformer achieves an Equal Error Rate (EER) of 0.16% on SVC2004 Task 2 and maintains an EER of 2.40% under the more challenging WD-5/5 setting. On MPIS-Sig, the model achieves over 98% verification accuracy across different writing postures. These results demonstrate that FT-Transformer effectively improves OSV robustness under data-scarce and multi-posture conditions.