用神经网络估算签名时的运动与力矩特征，提升验证准确率
Neural network modelling of kinematic and dynamic features for signature verification
- 用神经网络预测签名过程中的角速度、角度和力矩
- 在MCYT300数据集上训练，跨库验证表现稳定
- 无需昂贵设备，适合实际部署的签名识别系统
在线签名参数基于人体特征，扩大了自动签名验证的应用范围。尽管以往已提出运动学与动力学特征，但准确测量如手臂和前臂力矩仍具挑战。本文提出两种方法估算角速度、角位置和力矩：第一种利用物理UR5e机械臂复现签名并实时采集参数；第二种为低成本方案，采用神经网络估计相同参数。结果表明，简单神经网络模型可有效提取用于签名验证的关键特征。在MCYT300数据集上训练，并通过BiosecurID、Visual、Blind、OnOffSigDevanagari 75和OnOffSigBengali 75等数据库进行交叉验证，证实了模型良好的泛化能力。
原文摘要 · Abstract (English)
Online signature parameters, which are based on human characteristics, broaden the applicability of an automatic signature verifier. Although kinematic and dynamic features have previously been suggested, accurately measuring features such as arm and forearm torques remains challenging. We present two approaches for estimating angular velocities, angular positions, and force torques. The first approach involves using a physical UR5e robotic arm to reproduce a signature while capturing those parameters over time. The second method, a cost effective approach, uses a neural network to estimate the same parameters. Our findings demonstrate that a simple neural network model can extract effective parameters for signature verification. Training the neural network with the MCYT300 dataset and cross validating with other databases, namely, BiosecurID, Visual, Blind, OnOffSigDevanagari 75 and OnOffSigBengali 75 confirm the models generalization capability.
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