Handwritten Signature Verification Using Hand-Worn Devices

TL;DR

This paper introduces a new online signature verification method leveraging data from common hand-worn devices like smartwatches, achieving high accuracy in distinguishing genuine signatures from forgeries.

Contribution

The study presents a novel signature verification approach using motion sensor data from everyday wearable devices, replacing specialized digital signature capture tools.

Findings

Achieved 0.98 AUC in signature verification accuracy

Collected and tested on 1980 signature recordings from 66 subjects

Demonstrated high effectiveness of wearable device data for security verification

Abstract

Online signature verification technologies, such as those available in banks and post offices, rely on dedicated digital devices such as tablets or smart pens to capture, analyze and verify signatures. In this paper, we suggest a novel method for online signature verification that relies on the increasingly available hand-worn devices, such as smartwatches or fitness trackers, instead of dedicated ad-hoc devices. Our method uses a set of known genuine and forged signatures, recorded using the motion sensors of a hand-worn device, to train a machine learning classifier. Then, given the recording of an unknown signature and a claimed identity, the classifier can determine whether the signature is genuine or forged. In order to validate our method, it was applied on 1980 recordings of genuine and forged signatures that we collected from 66 subjects in our institution. Using our method, we were able to successfully distinguish between genuine and forged signatures with a high degree of accuracy (0.98 AUC and 0.05 EER).