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.

翻译：在线签名验证通常依赖于基于函数的特征，例如通过数字化仪获取的时间采样水平与垂直坐标，以及书写者施加的压力。虽然基于数字化仪数据推断关于书写者手臂姿态、运动学和动力学的额外信息可能很有用，但这构成了一项挑战。在本文中，我们通过提出一组基于在线签名动力学的新特征来应对这一挑战。这些新特征通过拉格朗日公式推导得出，获得了2D和3D机器人手臂模型的广义坐标序列与力矩序列。通过结合运动学与动力学机器人特征，我们的结果表明，它们在在线自动签名验证中具有显著的有效性，并且在集成到深度学习模型中时能够达到最先进的性能。