Neural network modelling of kinematic and dynamic features for signature verification

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 model’s generalization capability. The trained model is available at: https://github.com/gvessio/SignatureKinematics . • We explore kinematic and dynamic features for online signature verification. • A UR5 robotic arm is used to acquire these features from the MCYT330 dataset. • A neural network estimates the kinematic and dynamic features of a signature. • We demonstrate promising performance using the estimated features across datasets.