用拉格朗日力学建模签名动态，提升手写签名验证精度
Online Signature Verification based on the Lagrange formulation with 2D and 3D robotic models
- 基于拉格朗日力学构建2D/3D机械臂模型，提取签名动态特征
- 结合运动学与动力学特征，在公开数据集上达最新水平性能
- 适合研究签名识别、生物特征认证及机器人动力学应用者
在线签名验证通常依赖于函数型特征，如通过数字化仪获取的时间采样水平和垂直坐标以及书写压力。尽管基于数字化仪数据推断作者手臂姿态、运动学与动力学信息具有价值，但实现难度大。本文提出一种新特征集，基于在线签名的动力学特性，通过拉格朗日公式推导出2D和3D机械臂模型的广义坐标与力矩序列。结合运动学与动力学特征，实验表明该方法在自动签名验证中表现优异，集成至深度学习模型后达到当前最优结果。
原文摘要 · Abstract (English)
Online Signature Verification commonly relies on function-based features, such as time-sampled horizontal and vertical coordinates, as well as the pressure exerted by the writer, obtained through a digitizer. Although inferring additional information about the writers arm pose, kinematics, and dynamics based on digitizer data can be useful, it constitutes a challenge. In this paper, we tackle this challenge by proposing a new set of features based on the dynamics of online signatures. These new features are inferred through a Lagrangian formulation, obtaining the sequences of generalized coordinates and torques for 2D and 3D robotic arm models. By combining kinematic and dynamic robotic features, our results demonstrate their significant effectiveness for online automatic signature verification and achieving state-of-the-art results when integrated into deep learning models.
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