Abstract
In this paper, we propose SPECTRUM, a temporal-frequency synergistic model that unlocks the untapped potential of multi-domain representation learning for online handwriting verification (OHV). SPECTRUM comprises three core components: (1) a multi-scale interactor that finely combines temporal and frequency features through dual-modal sequence interaction and multi-scale aggregation, (2) a self-gated fusion module that dynamically integrates global temporal and frequency features via self-driven balancing. These two components work synergistically to achieve micro-to-macro spectral-temporal integration. (3) A multi-domain distance-based verifier then utilizes both temporal and frequency representations to improve discrimination between genuine and forged handwriting, surpassing conventional temporal-only approaches. Extensive experiments demonstrate SPECTRUM's superior performance over existing OHV methods, underscoring the effectiveness of temporal-frequency multi-domain learning. Furthermore, we reveal that incorporating multiple handwritten biometrics fundamentally enhances the discriminative power of handwriting representations and facilitates verification. These findings not only validate the efficacy of multi-domain learning in OHV but also pave the way for future research in multi-domain approaches across both feature and biometric domains. Code is publicly available at this https URL.
Abstract (translated)
在这篇论文中，我们提出了一种称为SPECTRUM的时频协同模型，该模型解锁了在线手写验证（OHV）中多领域表示学习未被充分利用的潜力。SPECTRUM包含三个核心组件： 1. **一个多尺度交互器**：通过双模态序列互动和多层次聚合，精细地结合时间与频率特征。 2. **自门控融合模块**：动态整合全局时间和频率特征，并通过自我驱动平衡实现这一过程。 这两个部分协同工作，实现了从微观到宏观的频谱-时间集成。 3. **一个多领域距离基验证器**：利用时间和频率表示来改进真实手写和伪造手写的区分度，超越了传统的仅基于时间的方法。 广泛的实验表明，SPECTRUM在现有的OHV方法中表现出色，强调了时频多领域学习的有效性。此外，我们揭示了整合多种手写生物特征从根本上增强了手写表示的判别力，并促进了验证过程。这些发现不仅证明了多领域学习在OHV中的有效性，而且为未来研究开辟了跨特征和生物特征领域的多领域方法的道路。 代码可在此 URL 公开获取：[https://this-url.com](https://this-url.com)
URL
https://arxiv.org/abs/2508.01427
