Owner authentication for mobile devices using motion gestures based on multi-owner template update

This paper proposes a template updating method for improving authentication accuracy in behavioral biometric authentication with hand/arm motion gestures for mobile devices. We introduce an extended version of the standard K-Medoids based clustering algorithm called supervised K-Medoids, which can h...

Full description

Saved in:
Bibliographic Details
Published in2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW) pp. 1 - 6
Main Authors Karita, Shigeki, Nakamura, Kumi, Kono, Kazuhiro, Ito, Yoshimichi, Babaguchi, Noboru
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2015
Subjects
Accuracy
mobile device
motion gesture
owner authentication
Servers
supervised K-Medoids
template update
Wrist
Online AccessGet full text
DOI10.1109/ICMEW.2015.7169873

Cover

More Information
Summary:This paper proposes a template updating method for improving authentication accuracy in behavioral biometric authentication with hand/arm motion gestures for mobile devices. We introduce an extended version of the standard K-Medoids based clustering algorithm called supervised K-Medoids, which can handle with 2-class data such as positive samples and negative samples. Using the supervised K-Medoids, the template corresponding to each owner is selected as the one that is the most identifiable as the actual owner, and, at the same time, the most distinguishable from the others. Therefore, our method can decrease False-Rejection-Rate (FRR) and False-Acceptance-Rate (FAR) simultaneously, compared to the conventional work that is based on the template update with only the owner's data to decrease FRR. Our template update with multi-owner data attains Equal-Error-Rate (EER) of 5.2% whereas the conventional template update method with owner's own data results in 12.0% when 10 subjects authenticate with gestures for 10 days.
DOI:10.1109/ICMEW.2015.7169873