Offline Handwritten Signature Verification Using a Stream-Based Approach

TL;DR

This paper introduces a novel, adaptive stream-based handwritten signature verification system that continuously learns from an ongoing sequence of signatures, outperforming traditional static models.

Contribution

It presents a new approach for HSV that adapts over time with an infinite data stream, addressing the dynamic nature of signatures and improving verification accuracy.

Findings

Superior performance over standard SVM-based methods

Effective on multiple benchmark datasets

Demonstrates adaptability to signature variability

Abstract

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.