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 2 s respectively 1 s. To the best of our
		  knowledge this work represents the first practical approach
		  towards acceleration based gait MOC authentication.<br>
<br><pre>@article{Findling_18_MobileMatchon,
  author = {Findling, Rainhard Dieter and H\"olzl, Michael and Mayrhofer, Ren\'e},
  title = {Mobile Match-on-Card Authentication Using
  		  Offline-Simplified Models with Gait and Face Biometrics},
  journal = { {IEEE} Transactions on Mobile Computing ({TMC})},
  year = {2018},
  volume = {14},
  number = {11},
  pages = {2578-2590},
  month = nov,
  issn = {1558-0660},
  doi = {10.1109/TMC.2018.2812883},
  keywords = {Mobile Computing, Authentication, Smart cards, Gait
  		  biometrics, Face biometrics},
  url = {https://ieeexplore.ieee.org/document/8307264/}
}
</pre>