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Life sciences · Preprint
arXiv · September 8, 2026
Posted before peer review. The findings may change or fail to hold.
This preprint describes a novel deep learning framework combining augmented path signatures and a T-Mamba model for online signature verification. The method reports state-of-the-art error rates on three public benchmarks, but the work is unrefereed and lacks validation in real-world authentication contexts or against human forgers.
Preprint. Signatures in public benchmark datasets; no human subjects involved. Intervention: Augmented path signature descriptor combined with T-Mamba model (hybrid design with two temporal convolutional network blocks and time-scanning Mamba). Compared with: Benchmark comparison on MCYT-100, SVC-2004 Task 2, and DeepSignDB (comparison method not specified).
Achieves state-of-the-art equal error rates (EERs) on three public benchmark datasets: MCYT-100, SVC-2004 Task 2, and DeepSignDB Framework demonstrates robustness especially when training data is limited Combines augmented path signature descriptor with T-Mamba model to capture both local temporal patterns and global long-range dependencies
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This is an unrefereed preprint describing a machine learning method for signature verification with reported benchmark results, but lacks peer review, clinical validation, or evidence of real-world deployment.
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Handwritten signature verification is vital for personal authentication across commercial and financial applications. Although deep learning methods are widely adopted for online signature verification (OSV), they often struggle with capturing highly discriminative features and modelling long-range dependencies. To address these issues, we propose a novel framework that integrates the augmented path signature (APS) descriptor with the T-Mamba model. The APS descriptor first applies time and basepoint augmentations, then computes sliding-window path signatures. The path signature is a non-parametric feature map from rough path theory that effectively captures geometric structures and nonlinear inter-channel interactions. Inspired by the efficacy of state space models (SSMs) in sequence modelling, our T-Mamba model employs a hybrid design combining two temporal convolutional network (TCN) blocks with a time-scanning Mamba. This design enables the model to learn both local temporal patterns and global long-range dependencies, substantially improving verification accuracy. Our framework achieves state-of-the-art EERs on three public benchmark datasets (MCYT-100, SVC-2004 Task 2, DeepSignDB), validating its effectiveness and robustness, especially when the training data is limited. Our code is publicly available at https://github.com/DLRL04/OSV-using-APS-and-T-Mamba.
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