Handwritten Signature Verification

1. Feature Extraction, Generation and Verification for Handwritten Signatures

Handwritten signature verification consists of three phases: feature extraction, feature generation and verification analysis. First, backbone networks extract static and dynamic features from signature images, including geometric structures, stroke dynamics (e.g., writing speed and pressure variation), and fine‑grained local traits such as cursive patterns. Next, feature dimensionality reduction, normalization or deep‑learning generation produces highly discriminative feature vectors. Finally, pattern matching algorithms such as Dynamic Time Warping, statistical models or neural networks compute similarity between test signatures and reference samples to determine authenticity, taking into account both the uniqueness of individual writing habits and abnormal traits of forgeries.

Furthermore, our laboratory applies state‑of‑the‑art techniques including diffusion models to generate skilled forgeries. These synthetic samples expand signature‑verification datasets and help distinguish genuine signatures from skilled forgeries, improving the robustness of handwritten‑signature verification systems.

2. Handwritten‑Signature Analysis under Multiple Scenarios

Multi‑scenario handwritten‑signature analysis adapts to diverse application environments (paper documents, electronic screens, mobile devices, etc.) and signature conditions (static images, dynamic pen‑pressure trajectories, multi‑period samples). For static scenarios, emphasis is placed on stroke shapes, ink distribution and local deformation, with image enhancement eliminating interference from lighting and recording media. Dynamic scenarios leverage temporal sequences (writing speed, acceleration, pressure curves) to construct behavioral biometric features, and cross‑modal fusion improves robustness. Transfer learning mitigates cross‑device data discrepancies. Hierarchical verification models incorporating contextual information (signature purpose, writing posture) differentiate natural handwriting variations from forgery traces, guaranteeing reliable verification for judicial documents, financial contracts and other real‑world scenarios.