Mobile Match-on-Card Authentication Using Offline-Simplified Models with Gait and Face Biometrics

Rainhard Dieter Findling, Michael Holzl, Rene Mayrhofer

Research output: Contribution to journalArticleScientificpeer-review

10 Citations (Scopus)
211 Downloads (Pure)

Abstract

Biometrics have become important for mobile authentication, 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. However, computational restrictions of SCs 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, wherefore we adapt features and model representation to enable their usage on SCs. The pre-trained model can be shipped with SCs on mobile devices without requiring retraining to enroll users. We apply our approach to acceleration based mobile gait authentication as well as face authentication and compare authentication accuracy and computation time of 16 and 32 bit Java Card SCs. Using 16 instead of 32 bit SCs has little impact on authentication performance and is faster due to less data transfer and computations on the SC. Results indicate 11.4% and 2.4-5.4% EER for gait respectively face authentication, with transmission and computation durations on SCs in the range of 2s respectively 1s. To the best of our knowledge this work represents the first practical approach towards acceleration based gait MOC authentication.

Original languageEnglish
Pages (from-to)2578 - 2590
JournalIEEE Transactions on Mobile Computing
Volume17
Issue number11
Early online date6 Mar 2018
DOIs
Publication statusPublished - Nov 2018
MoE publication typeA1 Journal article-refereed

Keywords

  • Acceleration
  • Adaptation models
  • Authentication
  • Biological system modeling
  • Biometrics (access control)
  • Computational modeling
  • Face biometrics
  • Gait biometrics
  • Mobile Computing
  • Mobile handsets
  • Smart cards

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