Capturing More: Learning Multi-Domain Representations for Robust Online Handwriting Verification

2025-08-02

AI Summary

To address the insufficient robustness in distinguishing genuine and forged signatures in online handwritten signature verification (OHV), this paper proposes SPECTRUM—a novel model that, for the first time, jointly models microscopic spectral (frequency-domain) and macroscopic temporal (time-domain) features. Methodologically, it introduces a multi-scale interaction module to extract dual-domain features, employs a self-gated fusion mechanism to dynamically weight and integrate multi-granularity time-frequency representations, and establishes a joint time-frequency distance metric framework. The core innovations lie in deep coupling of time- and frequency-domain features and a learnable cross-domain feature selection mechanism. Extensive experiments on multiple standard OHV benchmarks demonstrate that SPECTRUM significantly outperforms state-of-the-art methods, validating the substantial improvement in discriminative capability enabled by multi-domain representation learning. The source code is publicly available.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Feature Construction/Reformulation
• Application Domains: Humanities & Computational Social Science

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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 https://github.com/NiceRingNode/SPECTRUM.

Problem

• Enhancing online handwriting verification via multi-domain representation learning
• Integrating temporal and frequency features for robust handwriting discrimination
• Improving verification accuracy by combining multiple handwritten biometrics

Innovation

• Multi-scale interactor combines temporal and frequency features
• Self-gated fusion integrates global temporal and frequency features
• Multi-domain distance-based verifier improves handwriting discrimination