Mobile Match-on-Card Authentication Using Offline-Simplified Models with Gait and Face Biometrics
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
2578 - 2590
IEEE Transactions on Mobile Computing, Volume 17, issue 11
AbstractBiometrics 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.
Acceleration, Adaptation models, Authentication, Biological system modeling, Biometrics (access control), Computational modeling, Face biometrics, Gait biometrics, Mobile Computing, Mobile handsets, Smart cards
Findling , R D , Holzl , M & Mayrhofer , R 2018 , ' Mobile Match-on-Card Authentication Using Offline-Simplified Models with Gait and Face Biometrics ' , IEEE Transactions on Mobile Computing , vol. 17 , no. 11 , pp. 2578 - 2590 . https://doi.org/10.1109/TMC.2018.2812883