Handwritten Signature Verification

UbiComp 2018

Handwritten Signature Verification Using Wrist-Worn Devices

Alona Levy** Ben Nassi* Yuval Elovici* Erez Shmueli**

*Ben-Gurion University of the Negev **Tel-Aviv University

Abstract

This paper suggests a novel verification system for handwritten signatures. The proposed system is based on capturing motion signals from the sensors of wrist-worn devices, such as smartwatches and fitness trackers, during the signing process, to train a machine learning classifier to determine whether a given signature is genuine or forged. Our system can be used to: (1) Verify signatures written on paper documents, such as checks, credit card receipts and vote by mail ballots. Unlike existing systems for signature verification, our system obtains a high degree of accuracy, without requiring an ad hoc digital signing device. (2) Authenticate a user of a secure system based on "who you are" traits. Unlike existing "motion-based" authentication methods that commonly rely on long-term user behavior, writing a signature is a relatively short-term process. In order to evaluate our system, we collected 1,980 genuine and forged signature recordings from 66 different subjects, captured using a smartwatch device. Applying our signature verification system on the collected dataset, we show that it significantly outperforms two other state-of-the-art systems, obtaining an EER of 2.36% and an AUC of 98.52%.

Citation

@article{Levy:2018:HSV:3279953.3264929,

author = {Levy, Alona and Nassi, Ben and Elovici, Yuval and Shmueli, Erez},

title = {Handwritten Signature Verification Using Wrist-Worn Devices},

journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.},

issue_date = {September 2018}, volume = {2}, number = {3}, month = sep, year = {2018}, issn = {2474-9567},

pages = {119:1--119:26}, articleno = {119}, numpages = {26},

url = {http://doi.acm.org/10.1145/3264929},

doi = {10.1145/3264929},

acmid = {3264929}, publisher = {ACM},

address = {New York, NY, USA},

keywords = {Biometrics, Machine Learning, Online Signature Verification, Wearables},