提出流式签名验证方法，能持续学习新签名并适应变化。
Offline Handwritten Signature Verification Using a Stream-Based Approach
- 采用动态流式学习，实时更新签名模型。
- 在GPDS、CEDAR、MCYT上优于传统SVM方法。
- 适合长期运行的签名验证系统使用。
手写签名验证（HSV）系统用于区分真实签名与伪造签名。传统方法依赖静态批量数据，难以捕捉签名随时间变化的动态特性。签名具有高度类内差异，易受时间与外部因素影响。本文研究在数据流场景下的签名学习过程，提出一种自适应的HSV方法，可接收无限序列签名并持续更新。在GPDS Synthetic、CEDAR、MCYT数据集上的实验表明，该方法性能显著优于使用支持向量机（SVM）的标准方法。代码已开源：https://github.com/kdMoura/stream_hsv。
原文摘要 · Abstract (English)
Handwritten Signature Verification (HSV) systems distinguish between genuine and forged signatures. Traditional HSV development involves a static batch configuration, constraining the system's ability to model signatures to the limited data available. Signatures exhibit high intra-class variability and are sensitive to various factors, including time and external influences, imparting them a dynamic nature. This paper investigates the signature learning process within a data stream context. We propose a novel HSV approach with an adaptive system that receives an infinite sequence of signatures and is updated over time. Experiments were carried out on GPDS Synthetic, CEDAR, and MCYT datasets. Results demonstrate the superior performance of the proposed method compared to standard approaches that use a Support Vector Machine as a classifier. Implementation of the method is available at https://github.com/kdMoura/stream_hsv.
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