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Finger-drawn signature verification on touch devices using statistical anomaly detectors

  • Mudhafar M. Al-Jarrah
    ,
  • Shawq S. Al-Khafaji
    ,
  • Saad Amin
    ,
  • Xiaohua Feng
  • Middle East University, Jordan
    ,
  • Liwa College
Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Abstract

The use of behavioral biometrics in user authentication has recently moved to new security application areas, one of which is verifying finger-drawn signatures and PIN codes. This paper investigates the design of anomaly detectors and feature sets for graphic signature authentication on touch devices. The work involved a selection of raw data feature sets that are extracted from modern mobile devices, such as finger area, pressure, velocity, acceleration, gyroscope, timestamp and position coordinates. A set of computed authentication features are formulated, derived from the raw features. The proposed anomaly detector is based on the outlier method, using three versions of the Z-Score distance metric. The proposed feature sets and anomaly detectors are implemented as a data collection and dynamic authentication system on an Android tablet. Experimental work resulted in collecting a signature dataset that included genuine and forged signatures. The dataset was analyzed using the Equal-Error-Rate (EER) metric. The results for random forgery and skilled forgery showed that the Z-Score anomaly detector with 3.5 standard deviations distance from the mean produced the lowest error rates. The skilled forgery error rates were close to random forgery error rates, indicating that behavioral biometrics are the key factors in detecting forgeries, regardless of pre-knowledge of the signature's shape.

Publication Information

Output type

Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
 Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 1700-1705

Publication milestones

  • Published - 09/04/2020

Publication status

Published - 09/04/2020

Publisher

Institute of Electrical and Electronics Engineers Inc., United States

ISBN (Print)

9781728140346

Publication IDs

  • handle.net: 10547/624211
  • Scopus: 85083562610

Host publication title

2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)

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