research-article

Mobile Gait Match-on-Card Authentication from Acceleration Data with Offline-Simplified Models

Authors:

Rainhard Dieter Findling,

Michael Hölzl,

René MayrhoferAuthors Info & Claims

MoMM '16: Proceedings of the 14th International Conference on Advances in Mobile Computing and Multi Media

Pages 250 - 260

https://doi.org/10.1145/3007120.3007132

Published: 28 November 2016 Publication History

Abstract

Biometrics have become important for authentication on mobile devices, e.g. to unlock devices before using them. One way to protect biometric information stored on mobile devices from disclosure is using embedded smart cards (SCs) with biometric match-on-card (MOC) approaches. Computational restrictions of SCs thereby also limit biometric matching procedures. We present a mobile MOC approach that uses offline training to obtain authentication models with a simplistic internal representation in the final trained state, whereat we adapt features and model representation to enable their usage on SCs. The obtained model is used within SCs on mobile devices without requiring retraining when enrolling individual users. We apply our approach to acceleration based mobile gait authentication, using a 16 bit integer range Java Card, and evaluate authentication performance and computation time on the SC using a publicly available dataset. Results indicate that our approach is feasible with an equal error rate of ~12% and a computation time below 2s on the SC, including data transmissions and computations. To the best of our knowledge, this thereby represents the first practically feasible approach towards acceleration based gait match-on-card authentication.

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Cited By

Keykhaie SPierre S(2021)Lightweight and Secure Face-based Active Authentication for Mobile UsersIEEE Transactions on Mobile Computing10.1109/TMC.2021.3106256(1-1)Online publication date: 2021
https://doi.org/10.1109/TMC.2021.3106256
Findling RHolzl MMayrhofer R(2018)Mobile Match-on-Card Authentication Using Offline-Simplified Models with Gait and Face BiometricsIEEE Transactions on Mobile Computing10.1109/TMC.2018.281288317:11(2578-2590)Online publication date: 1-Nov-2018
https://doi.org/10.1109/TMC.2018.2812883

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cover image ACM Other conferences

MoMM '16: Proceedings of the 14th International Conference on Advances in Mobile Computing and Multi Media

November 2016

363 pages

ISBN:9781450348065

DOI:10.1145/3007120

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 28 November 2016

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MoMM '16: 14th International Conference on Advances in Mobile Computing and Multi Media

November 28 - 30, 2016

Singapore, Singapore

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Cited By

Keykhaie SPierre S(2021)Lightweight and Secure Face-based Active Authentication for Mobile UsersIEEE Transactions on Mobile Computing10.1109/TMC.2021.3106256(1-1)Online publication date: 2021
https://doi.org/10.1109/TMC.2021.3106256
Findling RHolzl MMayrhofer R(2018)Mobile Match-on-Card Authentication Using Offline-Simplified Models with Gait and Face BiometricsIEEE Transactions on Mobile Computing10.1109/TMC.2018.281288317:11(2578-2590)Online publication date: 1-Nov-2018
https://doi.org/10.1109/TMC.2018.2812883

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